Funding information Natural Sciences and Engineering Research Council of Canada; University of Manitoba; University of Manitoba Graduate Enhancement of Tri-Council Stipends

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Abstract

Cities are the planet's newest ecosystem and thus provide the opportunity to study community formation directly following major permanent environmental change. The human social and built components of environments can vary widely in different cities, yet it is largely unknown how features of cities covary with the traits of colonizing species despite humans being the ultimate cause of environments and disturbances in cities. We constructed a dataset from open-source data comprised of 13,502 breeding season observations of 213 passerine species observed in 551 Census-defined urban areas across the United States. We found that as a city became more compact with less sprawl it tended to support more migratory species and species with lower body mass, shorter lifespans, and larger clutches. We also found that species had lower body mass in cities with higher median income, and higher body mass in highly populated cities. Our results highlight the complexity of human-dominated urban ecosystems, where human socioeconomic actions and everyday activities intermix leading to structurally heterogeneous environments that support the colonization of some species over others.

INTRODUCTION

The recent growth of cities has created a natural experiment well suited for exploring how rapid land-use changes reshape animal communities. Urban landcover doubled between 1985 and 2015 growing from 362,747 to 653,354 km² (Liu et al., 2020). This rate of expansion outpaced the growth of the global human population during the same period (United Nations, 2019). It is now within cities that most people interact with and benefit from local biodiversity (e.g., through recreational activities and ecosystem services; Bolund & Hunhammar, 1999). Although cities can be a substantial threat to wildlife they also provide the opportunity for the conservation and management of native species (Spotswood et al., 2021). It is thus increasingly important for us to understand how cities support nature.

In natural settings, species with generally similar phenotypic traits tend to co-occur in similar environments (e.g., Pontarp et al., 2019; Vellend, 2016). This is a result of ecological selection (in the sense of Vellend, 2016)—the phenotype-based differential survival and reproduction of members of different species due to environmental filtering (Kraft et al., 2015) and biotic interactions. The ubiquity of humans in cities is the defining feature of a city's environment and our social composition and decision-making processes create urban environmental heterogeneity (Des Roches et al., 2020; Hobbs et al., 2006; Pickett et al., 2017; Schell et al., 2020). This heterogeneity is likely to cause ecological selection. A complete understanding of the evolutionary and ecological dynamics of urbanization requires us to identify the pathways through which human societies influence urban organisms (Des Roches et al., 2020; Schell et al., 2020). Here, we test for associations between aspects of a city's socioeconomic status and phenotypic traits of urban passerines at the species level.

Our focus on a city's socioeconomic status as a species filter emphasizes the importance of human activities and decision-making in urban ecosystems (Des Roches et al., 2020; Pickett et al., 2017; Schell et al., 2020). We chose this emphasis because humans, as the ultimate ecosystem engineers in cities, provide and remove resources, pollute the environment, and generally influence wildlife populations in both negative and positive ways (Gaston et al., 2014; Johnson & Munshi-South, 2017; Kyba et al., 2017; Shochat et al., 2006). Noise, light, and chemical pollution, impermeable surfaces, green spaces, and human supplemented food sources are all likely to vary with the number, density, and wealth of human populations. Consequently, focusing on humans should capture these likely highly variable sources of disturbance in a cohesive measure. However, research directly exploring the effects of cities' socioeconomic status on wildlife communities is still rare although various human activities that cause disturbance (e.g., traffic, different types of pollution) could have consistent effects on wildlife communities.

The consequences of human social and economic decisions scale from individual households to whole cities (Rigolon et al., 2018). We hypothesized that a city's compactness or sprawl, human population size, and median income were likely to capture the overarching patterns related to how human decisions might shape environments and thus animal communities. For example, plant diversity, vegetation cover, and food provisioning—important predictors of urban biodiversity—are higher in wealthier areas (Hope et al., 2003; Iverson & Cook, 2000; Kinzig et al., 2005; Leong et al., 2018; Talarchek, 1990). Sprawl, characterized by scattered, low-density development extending across vast areas of land, has become the prevailing development pattern for nearly all metropolitan areas across the United States (Bullard et al., 2000; Ewing et al., 2002; Huang et al., 2007), with wide-reaching effects on habitat availability and quality, biodiversity, and the health and well-being of people living in those cities (Bullard et al., 2000; Matlack, 1993; Robinson et al., 2005; Zuidema et al., 1996). Highly populated and sprawling cities will also be the most disturbed and polluted with high-traffic road networks connecting suburban areas to city centers. Migration is another important trait associated with resource availability and environmental variability that allows species to cope with seasonality and periods of low productivity, particularly among birds, and migratory species generally select resource-rich, stable habitats during the breeding season (Somveille et al., 2015). The use of cities by migratory species is relatively unexplored, but there is some evidence that migratory birds may be underrepresented in urban ecosystems (Allen & O'Connor, 2000; Kluza et al., 2000; Poague et al., 2000). We therefore also tested for associations between a city's socioeconomic status and the migratory status of the species found in that city.

There is now considerable evidence from both birds and mammals suggesting that cities filter for subsets of local species that have traits suited to population persistence in urban environments (Alberti et al., 2017; Aronson et al., 2016; Chace & Walsh, 2006; Croci et al., 2008; Hensley et al., 2019; Jokimäki et al., 2016; Kark et al., 2007; Leveau, 2013; Leveau et al., 2017; Meffert & Dziock, 2013; Meillère et al., 2015; Santini et al., 2019; Sepp et al., 2018; Silva et al., 2016; Sol et al., 2014). Much of this previous work has focused on urban–rural species trait comparisons that treat different cities as homogeneous and comparable with respect to the features of species that can colonize them. However, it is notable that in instances where species filtering has been explored across multiple cities, different cities seem to filter for slightly different subsets of traits (Hensley et al., 2019; Tryjanowski et al., 2013; however, see Morelli et al., 2016). This finding suggests that different cities are suited to hosting particular types of species. Our work differs from the more typical comparisons of urban and rural communities in that we treat cities as a spatially variable habitat type and explore trait variation across different cities.

We tested for associations between city socioeconomics and the median body size, lifespan, reproductive output, and migratory status of the species it hosted using a dataset we constructed from open-source data. Our overall goal was to investigate the effects of human activities on birds per se and we thus considered city features as general metrics of human social and socioeconomic activity. The socioeconomic status of cities is correlated with resource availability and environmental stability, with increasing human population size and sprawl positively correlated with disturbance, and the compactness of a city and increasing median income correlated with resource stability and abundance (Bullard et al., 2000; Leong et al., 2018; Matlack, 1993; Robinson et al., 2005; Zuidema et al., 1996). Natural environments characterized by disturbance tend to be positively correlated with species body size and lifespan and negatively related to reproductive output (Lack, 1947, 1954; Martin, 1987; Ricklefs, 2000). This is thought to be because (1) large-bodied species are better able to withstand periods of low resource availability; (2) investment in fewer, better-quality offspring in poor environments increases their likelihood of survival; and (3) being long-lived allows for multiple reproductive attempts given higher offspring mortality rates (Sol et al., 2012, 2014). We thought these same patterns would manifest across cities.

METHODS

Data compilation

We compiled our dataset from open data sources (see Appendix S1: Figure S1 for detailed data compilation process). First, we downloaded the eBird Basic Dataset for the United States from eBird.org (Sullivan et al., 2009). eBird is an online bird abundance and distribution checklist program jointly coordinated by the Cornell Laboratory of Ornithology and the National Audubon Society. The eBird project relies on citizen science volunteer observers who submit georeferenced observations of species to a centralized database. Observations are submitted in a checklist format, listing species seen during one bird-watching occasion. Checklists are designated as either complete checklists, where all birds detected and identified were recorded, or incomplete checklists, where some species seen were not recorded. Regional reviewers identify outliers and verify each species observation based on sighting coordinates (Wood et al., 2011). We used eBird observations of passerines from the United States so that we were working with related and trophically similar species groups across cities with relatively comparable histories. We focused on passerines because they are a broadly comparable group with many species that have colonized cities. More precisely, we focused on native passerines noted as being present in cities during the breeding season. We discarded observations of transient birds likely on route to their breeding grounds and observations of birds from outside of the breeding season. We chose observations from 27 May to 7 July as our breeding season, following the practices of the North American Breeding Bird Survey (available from https://www.pwrc.usgs.gov/bbs/index.cfm). We note that our species are found in cities during the breeding season but may not necessarily breed in cities. Our focus on the breeding season ensured that our observations were focused on a period of high resource demand. Our observations were filtered to match data available for city socioeconomic status metrics which were last measured in 2010. This period also allowed for the maximum accumulation of species detections in cities. The observation dates were filtered using the R package auk (version 0.3.3; Strimas-Mackey et al., 2018) in R version 3.5.0 (R Core Team, 2018).

We extracted species-level data for body mass, clutch size, and longevity from the Amniote Life-History database (Myhrvold et al., 2015). This is a systematically compiled database of life-history traits for birds, mammals, and reptiles built for comparative life-history analyses. For species with multiple raw data points, the median value is reported. There were no obvious mistakes in this database for our species. We compared species lists from eBird and the Amniote Life-History database and found 17 species of passerines present in the eBird dataset that were not found in the Amniote database (Appendix S1: Table S1). A few of these were introduced species, some were species with a limited range or a specific habitat type, and most were unlikely to breed in cities. So, overall, not many species were lost using the data from the Amniote Life-History database. Bird species that are typically present in a single US state year-round were classified as residents, and bird species that migrate to breed were classified as migrants. Some populations of migratory birds can choose to stay in cities overwinter and therefore could be classed as residents, but our data did not allow the determination of these kinds of population-level differences.

We assigned georeferenced eBird species records to urban areas using US Census-defined urban area maps provided by the U.S. Census Bureau (2010a). The urban area shapefiles define an urban area as a densely developed territory with at least 2500 people (U.S. Census Bureau, 2010a). We used the R packages sp (version 1.3-1; Bivand et al., 2013), rgdal (version 1.3-4; Bivand et al., 2018), and maps (version 3.3.0; Becker & Wilks, 2018) for this merge. Next, we obtained socioeconomic features for these urban areas using the 2010 Census data from the U.S. Census Bureau (2010b). Data were available for the year 2010 and not the full span of our bird observation data. We thus assume that these data have remained comparable. We chose the human population size, median household income, and sprawl/compactness as our general metrics of human social and socioeconomic activity. We calculated a city's sprawl by dividing the human population size in each city by the city's area—a generally recognized measure of sprawl (Huang et al., 2007).

We were interested in species present in an urban area during a breeding season, not the number of observations of each species nor estimates of species abundance, and so our final data set was a list of each species observed in each city during our study period. While questions about abundance are certainly interesting, we considered species-, and city-specific abundance estimates to be beyond the scope of our questions about species-level trait variation. Additionally, our decision to observe the effects of socioeconomics across cities does not enable us to investigate within city variation, although legacy effects due to cultural and political decisions (e.g., racism, economic collapse) may have considerable influence on current biodiversity patterns and processes in cities (Schell et al., 2020; Shackleton & Gwedla, 2021). Furthermore, habitat quality and the availability of resources in a city can also change over time as cities age and there is a shift in human behavior and preferences, leading to a change in species responses (Spotswood et al., 2021). For example, our city metric, median household income, is the median household income for each city in 2010. It is thus not possible to look at change over time, although changes in the economic situation could certainly be possible for some cities. We excluded non-native, introduced, and pet traded species, as well as accidental observations from our dataset (see Appendix S1: Table S2). We removed observations that were part of the birds' overwintering range, observations of transient birds, and vagrants from each state.

Data analyses

All analyses were conducted using R version 3.5.0 (R Core Team, 2018). To determine whether eBird surveys adequately sampled species using cities during our study period, we plotted the accumulation of species in selected cities over the years 2010–2018 (see Appendix S1: Figure S2). These accumulation curves leveled off for the majority of the cities plotted, indicating that the eBird surveys were capturing most species found in a city. Human population size, compactness/sprawl, and median household income were not strongly correlated (Appendix S1: Figure S3) and were treated as independent variables in a series of mixed models that used species traits as dependent variables. These species-level traits were median clutch size, median longevity (the lifespan of an individual in years), median body mass (mass of an individual in grams), and migratory status (migratory or resident). Data were scaled to standardize the range of independent and dependent variables to make model effects comparable.

Clutch size, longevity, and body mass were treated as dependent variables in a series of linear mixed-effects models (LMMs) using the function lmer in R package lme4 (version 1.1-21; Bates et al., 2014). All urban variables were fit in each model. We also included taxonomic family and US state as random effects allowing intercepts to vary. Random intercepts estimate between-group variation in means, as well as variation within groups in each of our dependent variables. The taxonomic family was added as a random effect to account for variation within passerine families. Phylogenetic analyses are used to account for the possibility that unreplicated evolutionary events in species evolutionary histories explain contemporary patterns. As we were interested in relationships between species traits and the features of cities, we thus did not control species similarities. This was because urbanization is a contemporary phenomenon—a species that persists in a city is doing so because of the ecology associated with its traits in contemporary timeframes, regardless of its phylogenetic origin. Our aim is to explore city-species trait relationships rather than control for them. Treating family as a random effect allows us to compare like species with like species and then estimate overall general trends across species. This approach allows us to find that phylogenetic signals exist, albeit not estimating it precisely, without statistically controlling for the patterns we were interested in. As socioeconomic variation seemed likely to strongly depend on local governance, we included state as a random effect in our models. These hierarchical models account for state-level variation in socioeconomics and statewide and across state variation in climate and can be interpreted as fitting state by state models and then generalizing effects across the states. Model residuals were plotted against the expected values and we saw no strong violations of the models' assumptions, except for body mass. We log₁₀-transformed body mass to ensure the normality of residuals.

Migratory status was a binary variable and so we used a generalized LMM with a binomial error structure and logit link function for this analysis. The model structure was similar to that for LMMs, with family and state treated as random effects and the city traits fit as independent variables. Migratory status was coded 1 for migratory species and 0 for residents.

We tested the residuals of our models for spatial autocorrelation using the R packages spdep (Bivand et al., 2013) and adespatial (Stéphane et al., 2020). We first computed the maximum distance of the minimum spanning tree, which is the minimum value that keeps all samples connected, and then built a connectivity matrix among sites by identifying the nearest spatial neighbors using this threshold value (Borcard et al., 2011). We then used this spatial weights matrix to calculate the Moran's I correlation coefficient for each of our models. Spatial autocorrelation in model residuals violates the assumption that residuals are independent and identically distributed, which can bias parameter estimates and can increase Type I error rates (Dormann et al., 2007).

As species with a few sightings in a city may have been incorrect observations or vagrants, we ran our analysis with three different sampling criteria: (1) keeping all observations of species observed at least once in a city (see Appendix S1: Figures S4 and S5; Table S3); (2) keeping only observations of species observed 10 or more times in a city; and (3) keeping only observations of species observed over 20 times in a city (see Appendix S1: Figures S6 and S7; Table S4). For analyses presented in the main text, we chose the second sampling criteria and kept observations of species that were observed 10 or more times in a city—and included both incomplete and complete checklists. We also ran our analyses using complete checklists only (see Appendix S1: Figures S8 and S9; Table S5), which also gave similar results.

RESULTS

Our final dataset was comprised of 13,502 observations of 213 bird species from 29 families identified 10 or more times in 551 cities during at least one breeding season (Figure 1). We used the position of effect sizes and the breadth of confidence intervals (CI) to assess relationships (Nakagawa & Cuthill, 2007), and the conditional and marginal R² values, and the intra-class correlation coefficients to assess model fit (Nakagawa et al., 2017; Nakagawa & Schielzeth, 2013) (Tables 1 and 2). Conditional R² considers the variance explained by both fixed and random factors, marginal R² considers variance explained by fixed factors. The intra-class correlation coefficient calculates the proportion of variance explained by each random effect. City compactness was negatively related to species longevity (−0.056, CI = −0.076, −0.035) and body mass (−0.025, CI = −0.037, −0.013) and positively related to clutch size (0.021, CI = 0.005, 0.037) (Figure 2; see Table 1 for full model results). This means that as a city became more compact, it supported more species with shorter lifespans, lower body masses, and larger clutches. Median income was negatively related to species body mass (−0.012, CI = −0.021, −0.004) while human population size was positively related to body mass (0.014, CI = 0.003, 0.025) (Figure 2; Table 1). As migratory status was binary data, where species were classed as migratory (1) or resident (0) we used odds ratios and their confidence intervals to assess support for a relationship. An odds ratio greater than one describes a positive relationship, and therefore our results show that the likelihood of species being migratory increased in more compact cities (1.461, CI = 1.332, 1.602) (Figure 3; Table 2). There were no other detectable relationships identified in our models (Figures 2 and 3; Tables 1 and 2).

Details are in the caption following the image — **FIGURE 1**
Open in figure viewer PowerPoint

Map of 13,502 bird observations of 213 passerine species in 551 cities across the United States. Circles represent the longitude and latitude coordinates of bird observations

TABLE 1. Model summaries for the urban predictors of passerine life-history traits in the United States

Response	Predictors	β	95% CI	R²_c	R²_m	ICC_[State]	ICC_[Family]
Longevity	Intercept	−0.422	−0.713, −0.131	0.552	0.033	0.013	0.538
	Compactness	−0.056	−0.076, −0.035
	Median income	−0.004	−0.018, 0.010
	Population size	−0.002	−0.020, 0.016
Clutch size	Intercept	0.400	−0.049, 0.849	0.826	0.000	0.02	0.824
	Compactness	0.021	0.005, 0.037
	Median income	0.005	−0.005, 0.016
	Population size	−0.014	−0.028, 0.001
Body mass	Intercept	3.033	2.725, 3.341	0.779	0.030	9.851e-04	0.778
	Compactness	−0.025	−0.037, −0.013
	Median income	−0.012	−0.021, −0.004
	Population size	0.014	0.003, 0.025

Note: One model was fit to data per response variable, including all the urban characteristics: city compactness/sprawl, median income, and human population size. Body mass was log₁₀-transformed. Random effects were specified as (1 | family) and (1 | state), where each level of the grouping factors, family and state, had its own random intercept. We also report the conditional and marginal R² values and the intra-class correlation coefficients. Conditional R² considers the variance explained by both fixed and random factors, marginal R² considers variance explained by fixed factors. The intra-class correlation coefficient calculates the proportion of variance explained by each random effect. The number of observations was n = 13,502 for all the life-history variables.
Abbreviations: β, coefficient estimate; CI, confidence interval; ICC, intra-class correlation coefficient; R²_c, conditional R²; R²_m, marginal R².

TABLE 2. Model summaries for the urban predictors of passerine migratory status in the United States

Response	Predictors	Odds ratios	95% CI	R²_c	R²_m	ICC_[State]	ICC_[Family]
Migratory status	Intercept	0.267	0.035, 2.011	0.897	0.003	0.009	0.887
	Compactness	1.461	1.332, 1.602	0.05	8.03
	Median income	1.033	0.972, 1.1	0.03	1.04
	Population size	0.930	0.849, 1.020	0.04	−1.54

Note: One model was fit to data per response variable, including all the urban characteristics: city compactness/sprawl, median income, and human population size. Body mass was log₁₀-transformed. Random effects were specified as (1 | family) and (1 | state), where each level of the grouping factors, family and state, had its own random intercept. We also report the conditional and marginal R² values and the intra-class correlation coefficients. The number of observations was n = 13,298.
Abbreviations: CI, confidence interval; ICC, intra-class correlation coefficient; R²_c, conditional R²; R²_m, marginal R².

Spatial autocorrelation

There was no spatial autocorrelation in the model residuals for longevity (p = 0.22, Moran's I = 4.27e−05), clutch size (p = 0.44, Moran's I = −5.14e−05) or body mass (p = 0.26, Moran's I = 2.58e−05) (see Appendix S1: Figure S10 for maps of spatial variation in passerine life-history traits and spatial dependency in model residuals for each model across the United States). The model residuals for migratory status were statistically significant, but Moran's I was negligibly low (p = 2.83e−06, Moran's I = 6.26e−04).

DISCUSSION

The lifespans, body masses, clutch sizes, and migratory strategies of species found in different cities all varied with the degree of city sprawl, and the body masses of species varied with median income and population size. These relationships presumably reflect differences in the average fitness of a species with these traits given the local features of a particular city. This suggests that phenotype-based differential survival of species—in this case related to environmental factors associated with a city's socioeconomics—plays a role in determining biodiversity at the earliest stage of community formation following the emergence of new environments. Like environmental variation in natural habitats, cities vary in the details of their composition, and different species-level traits and migratory strategies are likely better suited to different types of cities.

Cities are heterogeneous ecosystems that vary in their social, socioeconomic, biological, and physical components (Alberti, 2015; Grimm et al., 2008; Schell et al., 2020; Szulkin et al., 2020), and urban ecological and evolutionary processes can be influenced by both past and present human activities (Clarke et al., 2013; Des Roches et al., 2020; Roman et al., 2018). Social inequities in cities, such as relationships between urban vegetation cover, temperature, pollution, and race can influence urban biodiversity patterns and the structure and composition of the urban area as a whole (Clarke et al., 2013; Jesdale et al., 2013; Roman et al., 2018; Schell et al., 2020; Tessum et al., 2019; Watkins & Gerrish, 2018). Sprawl is largely a product of such societal and sociopolitical processes (Bullard et al., 2000; Huang et al., 2007). We found that more compact cities with less sprawling suburbs tended to support smaller, short-lived species with larger clutches.

In general, more natural areas with plentiful and stable resources tend to support greater numbers of small-bodied species that prioritize reproduction at the expense of lifespan, perhaps due to high offspring survival rates (Bennett & Owens, 2002; Bielby et al., 2007; Sæther & Bakke, 2000). Stochastic or resource-poor environments tend to support larger, longer-lived species that prioritize adult survival over producing many offspring. This strategy is thought to buffer against the consequences of reproductive failures by spreading the risk of offspring mortalities across multiple breeding attempts during a long lifetime. In our case, species body mass, clutch size, and lifespan varied together as expected if structurally compact cities were relatively resource-rich and stable environments for birds. Urban sprawl is highly correlated with housing density (Appendix S1: Figure S3): densely housed areas tend to be compact, with housing concentrated in and around city centers. Areas with lower housing density are generally more scattered (Ewing et al., 2002). The remaining natural areas in sprawling low-density cities are generally highly fragmented, degraded, and isolated. Consequently, they might have less vegetation and less connected green space than more compact cities (Marzluff & Ewing, 2001; Robinson et al., 2005). Lower levels of sprawl may thus indirectly lead to more resources for birds particularly on a citywide scale.

The results for migratory species support our resource and environmental variability-based interpretation of urban sprawl. More compact urban areas tended to support relatively more migratory species, whereas the median income or human population size of a city was not obviously important. Migratory species generally choose breeding sites with suitable resources for breeding (Dalby et al., 2014; Somveille et al., 2015, 2019). That more migratory species are found and choose to breed in low sprawl cities suggests that they may be choosing these cities, at least in part, based on resource availability (Faaborq et al., 2010; Jenkins et al., 2017; Martin & Karr, 1986).

We also found that species body mass was negatively related to median income and positively related to human population size. Body mass is generally thought to correlate with several demographic traits (Sæther, 1987; Western & Ssemakula, 1982), yet we found no evidence of such covariation with longevity or clutch size when looking at median income and population size (see also Sol et al., 2014).

A recent study exploring species filtering across three cities also found that different cities seemed to filter for slightly different subsets of traits—notably diet guild, habitat preference, and migratory status, suggesting that species filtering in cities is at least partly related to resource availability (Hensley et al., 2019). This finding, in conjunction with our spatially extended analyses, suggests that human actions and socioeconomics can shape trait groups within urban bird communities based on resource availability and variability so that species with similar phenotypic traits tend to co-occur in similar environments. Other factors, such as climate, temperature, the amount of green space, or the amount of open habitat in the city likely also drive species observations in urban areas, as found in previous studies (Filloy et al., 2015, 2019; Lee et al., 2019). These factors are likely in part correlated with the socioeconomic variables we measure and they will also contribute to the unexplained variation in our data.

Cities, and particularly human activities in cities, provide resources and disturb birds in many ways. In more compact cities, housing density and accompanying roads, traffic, and pollution, among other things, are generally more concentrated near city centers. This may lead to more intact less fragmented remnant vegetation patches in those cities, and less noise and light pollution outside the city center. Wealthier areas generally have higher plant diversity and vegetation cover—a well-known pattern called the “luxury effect”—and people in wealthier cities may provide more supplemental food, shelter, and nesting habitats for urban birds (Hope et al., 2003; Iverson & Cook, 2000; Kinzig et al., 2005; Leong et al., 2018; Talarchek, 1990). Wealth effects may present differently across cities, however; wealthier cities generally have more extensive road systems and more sprawling urban areas than poorer cities, which may limit the resources available for birds (Huang et al., 2007). However, wealthier cities also have more open space that corresponds to parks, vegetation, and water (Huang et al., 2007). On the other hand, highly populated and sprawling cities may be the most disturbed in terms of traffic levels, noise, artificial light, and pollution (Isaksson, 2018; Luck, 2007; McKinney, 2001, 2002; Strohbach et al., 2019). This might make these areas most suitable for birds with traits that are advantageous in unstable environments. These habitats might also provide anthropogenic food sources via bird feeders and human food waste (Lepczyk et al., 2004; Tryjanowski et al., 2015) which can influence urban bird community structure and breeding success (Galbraith et al., 2015; Robb et al., 2008). The diverse plant communities found in backyards and gardens in sprawling suburban neighborhoods can also provide important sources of food, cover, and nesting resources for birds able to thrive in human-dominated landscapes (Narango et al., 2018; Smith et al., 2005, 2006; Thompson et al., 2003). Consequently, although cities are generally more similar to each other than to their natural surroundings, our results suggest that from a bird's eye view, they are heterogeneous habitats supporting different life-history and migratory strategies that in part depend on socioeconomic factors and the built environment. It is worth noting that our method of using species presence in cities as our observations could not distinguish between primarily urban-dwelling species and transient species visiting cities from surrounding areas and so it is possible that not all species are fully urban colonizers (Evans et al., 2011; Sol et al., 2014). Nevertheless, it seems likely that individuals choosing to enter a city are treating it as a resource, especially during energetically taxing breeding seasons, and thus our species are city users if not necessarily urban breeding species.

However, we note that even though cities may provide resources to some birds, cities also harbor considerable threats to birds. Cats, often let to roam free in suburban areas, are the main source of mortality to birds in cities, resulting in 2.4 billion deaths in the United States yearly (Loss et al., 2013). Window collisions are also a major source of bird mortality in cities killing millions of birds annually (Calvert et al., 2013; Machtans et al., 2013). Additionally, the food resources found in cities may be of lower quantity or quality than those found in natural areas (Pollock et al., 2017, but see Seewagen et al., 2011). It is, therefore, possible that cities attract birds, but could act as ecological traps—birds might actively choose cities to breed in, and that decision might then reduce their fitness (Hale & Swearer, 2016).

Generally, our results highlight the complexity of human-dominated urban ecosystems, where human socioeconomic factors and everyday activities intermix leading to structurally complex environments that support the colonization of some species over others. The proximate causes of urban environmental variation are likely to vary for different cities and species. Nevertheless, species traits can be predicted by the ultimate cause of urban environments—human activities. As cities are the world's most rapidly growing ecosystem, understanding what kinds of species initially colonize cities provides important information about how the distribution of biodiversity will change following rapid, human-caused environmental shifts in an era of global change. Finally, although cities tend to support fewer species than nearby natural areas (Chace & Walsh, 2006), on average urban biodiversity is primarily comprised of native species (Aronson et al., 2014). These findings in addition to our results suggest that cities could play a more important role in conservation and management than they currently do.

ACKNOWLEDGMENTS

The authors want to thank the members of the Population Ecology & Evolutionary Genetics Group for their helpful comments on manuscript drafts. The authors also acknowledge all the work done by the eBird citizen-science observers. This study was supported by a Natural Sciences and Engineering Research Council of Canada Discovery Grant to Colin J. Garroway. Riikka P. Kinnunen and Chloé Schmidt were additionally supported by the University of Manitoba Graduate Fellowships and a University of Manitoba Graduate Enhancement of Tri-Council Stipends funding grant to Colin J. Garroway.

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest.

Open Research

DATA AVAILABILITY STATEMENT

Data (Kinnunen et al. 2021) are available from Dryad: https://doi.org/10.5061/dryad.ncjsxksw7.

Supporting Information

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Volume13, Issue2

February 2022

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The socioeconomic status of cities covaries with avian life-history strategies

Abstract

INTRODUCTION