It's not just noise: The consequences of inequitable noise for urban wildlife

doi:10.21203/rs.3.rs-2753779/v1

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It's not just noise: The consequences of inequitable noise for urban wildlife

https://doi.org/10.21203/rs.3.rs-2753779/v1

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published 20 Nov, 2023

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Understanding how systemic biases influence local ecological communities is essential for developing just and equitable environmental practices that center both human and wildlife wellbeing. With over 270 million United States residents inhabiting urban areas, understanding the socio-ecological consequences of racially-targeted zoning, such as redlining, will prove critical for urban planning. There is a growing body of literature documenting the relationships between redlining and the inequitable distribution of environmental harms and goods, green space cover and pollutant exposure. However, it remains unknown whether historical redlining impacts the distribution of urban noise or whether inequitable noise drives an ecological change in urban environments. We conducted 1) a spatial analysis of how urban noise corresponds with the distribution of redlining categories and 2) a systematic literature review to summarize the effects of noise on wildlife in urban landscapes. We found strong evidence that noise is inequitably distributed in redlined urban communities across the United States, and that inequitable noise may drive complex biological responses across diverse urban wildlife, reinforcing the interrelatedness of socio-ecological outcomes. These findings lay a foundation for future research that advances relationships between acoustic and urban ecology through centering equity and challenging systems of oppression in wildlife studies.

Biological sciences/Ecology

Earth and environmental sciences/Environmental social sciences/Environmental impact

Biological sciences/Ecology/Urban ecology

Redlining

noise pollution

environmental justice

urban ecology

With approximately 270 million people in the United States residing in urban and suburban areas ¹,humans continually shape and are themselves shaped by urban environments. Urban ecologists have long recognized the unique links between social and ecological processes that occur in urban environments ². Social processes, such as systemic biases that shape institutional policies and urban planning practices, can directly influence biological patterns ^3–6. One example of systemic bias structuring the urban landscape is residential segregation or the spatial separation of two or more demographic groups within a city ⁷. Residential segregation stems from a combination of complex political and socio-economic factors, including uneven industrial development, discrimination in real estate lending, urban zoning, inequitable government financing, and workplace discrimination ^8–11. Redlining was one of the most blatant forms of residential segregation (occurring in the United States from 1933–1968), which delineated neighborhoods largely by race into ordinal grades from A–D. Through this grading process, Black Americans and other people of color were excluded from obtaining loans to purchase homes in A and B neighborhoods and were forced to reside in C and D neighborhoods that received limited investment from governments and banks ¹². These Home Owners' Loan Corporation (HOLC) grades, along with other mechanisms of residential segregation, have had significant legacy effects that persist today as social and environmental inequities.

Communities of color in presently or previously segregated neighborhoods continue to have the highest incidences of urban poverty in the United States and are disproportionately impacted by environmental injustices ^13–16. The interactions between a city’s historical and present-day policies and practices work to generate these observed patterns in environmental inequities. While we focus here on the historical practice of redlining, such practices may include formal segregation through covenants, the intentional placement of hazardous pollutants and weak regulatory enforcement in marginalized communities, and limited opportunities for communities of color in decision-making ^10,17–19. These inequities in urban planning practices have driven disproportionately high toxin exposure in communities of color and of low socio-economic status, leading to well-documented elevated health risks including increased rates of diseases such as asthma and cancer as well as higher mortality rates ^20–27. The environmental justice literature frequently demonstrates that race, class, and redlining are strongly correlated with the distribution of environmental hazards and benefits ^13,28,29. Evidence from over 37 cities indicates that redlined neighborhoods have greater exposure to environmental hazards like air pollution, toxic waste, heat islands, and flood risk while having decreased access to environmental necessities, such as parks and tree cover ^17,30. Previously redlined communities categorized as Grade D have been shown to have 21% less access to tree cover than neighborhoods established in Grade A areas while also having reduced overall greenspace (inclusive of shrubs and grasses) in comparison to Grade A communities as estimated by NDVI ^22,30. Lack of access to greenspace can confound issues associated with increased exposure to pollutants, as greenspaces provide many critical ecosystem services such as air pollution removal, carbon sequestration, and reduction of the heat island effect ^31–33. Despite the proliferation of research on the impacts of environmental injustices on human health, researchers are only beginning to understand and predict the consequences of these social inequities for ecological communities ⁶.

Recent evidence suggests that the inequitable distribution of tree and green space cover, environmental pollutants, impervious surfaces, and urban heat islands across gradients of redlining may explain patterns in ecological communities and wildlife behavior ⁶. These predictions are predicated on the ‘luxury effect’, defined as the association between greater socio-economic status and increased urban vegetation (i.e., higher urban vegetation cover and diversity, higher tree canopy cover, and greater abundance, size, and distribution of urban woodlands, parks, and green spaces) – all of which can influence patterns of animal diversity ^34–37. However, support for the luxury effect has been mixed ³⁷, as inequality in biodiversity distribution across socio-economic gradients is often differentially related to city spatial structure, population density, social policy, geographic variation in climate conditions, and/or human preference ³⁸. For example, highly developed downtown urban centers often have less room for vegetation ³⁹, while neighborhoods in less developed residential city edges may support greater biodiversity because residents have larger yards to convert into resources that support biodiversity ^38,40,41. Thus, urban population density may be a stronger predictor of biodiversity than socio-economic status in many cases ³⁸. Moreover, positive relationships between biodiversity and socio-economic status may be more prevalent in arid regions, given the higher relative cost of planting and irrigating trees and parks ³⁸. Previous research on the luxury effect has focused largely on understanding associations between socio-economic status and biodiversity, overlooking the intersection of biodiversity, redlining, and environmental injustices other than inequitable green space cover ⁶.

Noise pollution is one factor of the human-built environment that has received far less attention in the urban socio-ecological literature, despite well-documented human health impacts ⁴². Noise pollution propagates over considerable distances from industrial zones and road, rail, and airline networks, and is predicted to continue to increase ⁴³. Over the last decade (2010-2021) traffic volume has increased by 8% in the United States — faster than human population growth ⁴⁴. Noise pollution is so extensive that nearly 90% of the contiguous United States experiences noise above 30 decibels (dB) ⁴⁵, and over 100 million Americans are exposed to noise levels above 70 dB – the baseline level at which severe, damaging health effects are almost certain to occur ⁴². At such elevated levels, humans experience an increased risk of heart disease, hypertension, hearing degradation, endocrine disruption, decreased learning, productivity, and sleep quality, and elevated annoyance and stress ^42,46,47. Even noise levels of 50 dB can contribute to negative health effects ⁴⁸. Meanwhile, these effects are compounded for communities of color and of low-income, both subject to mechanisms of segregation that expose them to greater noise pollution than wealthy, predominantly white communities ^49–51. However, the limited research on inequitable noise to date has not explicitly evaluated how racially-targeted zoning policies, such as redlining, influence the distribution of noise pollution, and how this noise distribution connects to urban ecology.

Just as noise has substantial impacts on human health, the effects of noise on wildlife are also varied and extensive. Since 1990, hundreds of studies have documented individual and population-level responses to noise across a wide range of terrestrial and aquatic taxa ⁴³. The effects of noise on wildlife tend to be more severe and more heavily supported as noise levels increase from ~40 dB to over 100 dB ^43,52, indicating that both the extent and severity of noise exposure can drive changes in urban animal communities. Such changes include altered animal communication, movement and foraging behaviors, and shifts in distributions, community structure, and predator-prey interactions ^43,44. Adverse physiological impacts to reproduction and stress have also been documented ^43,53, which may have substantial consequences for individual fitness, including altered energy budgets, increased predation risk, and loss of vital sound cues ^53–55. With so many documented impacts of noise to wildlife, it is perhaps unsurprising that a recent meta-analysis found that noise affects most species studied, rather than just a few sensitive species⁵⁶. Thus, noise represents a pervasive driver of ecological change.

Growing evidence suggests that residential segregation, noise pollution, and human health disparities are interrelated ⁵⁷, but we do not yet fully understand how these relationships connect with urban ecological systems. The consistent evidence of noise pollution’s pervasive effects on a diverse array of taxa ⁴³ suggests that elevated noise may represent an invisible, but important factor shaping the distribution and behavior of urban animals. Elevated noise levels in residentially segregated neighborhoods may interact with decreased green space cover and other environmental inequities to further reduce biodiversity and habitat suitability, altering ecological processes, with possible impacts to both humans and wildlife. Understanding how inequitable noise impacts urban biodiversity and ecological processes is valuable to humans as exposure to natural sounds can positively affect human health by reducing stress and annoyance while enhancing mood and cognitive performance ⁵⁸. Thus, redlined neighborhoods may experience a lower standard of health due to the physical and mental effects of the noise itself and the decreased interaction with biodiverse spaces that improve health outcomes.

To fill critical knowledge gaps regarding the role of inequitable noise pollution in shaping urban biodiversity, we quantified the distribution of noise across HOLC grades. We focused on redlining instead of other measures of residential segregation (e.g., indices of the five dimensions of residential segregation;⁷) because redlining is explicitly racially-targeted and outcomes of redlining can be directly linked to racial bias. Additionally, historical redlining patterns are strongly associated with present-day racial segregation and income inequality ⁵⁹. Next, we conducted a literature review to assess the current state of knowledge on impacts of noise pollution on urban wildlife. Lastly, we synthesized potential impacts of inequitable noise on urban wildlife using the results from both the redlining noise analysis and the literature review.

Noise distribution across HOLC redlining grades

We summarized noise levels across redlining HOLC grades in 83 U.S. cities based on estimates of daily (averaged over 24-hours) exposure to transportation noise ⁶⁰. The mean excess noise level (N mean; an area-weighted metric of average transportation noise energy in A-weighted decibels >35 dBA averaged over a 24-hour period with units of dBA/900m²; see Methods for calculation) per 30m x 30m pixel across all cities ranged from 0.17-69.9 with a mean of 20.6 ± 0.8. The mean noise level ranged from 47.4-58.5 dBAand had an overall mean of 52.3 ± 0.09 dBA, without accounting for area differences. The best-fitted linear model found that both HOLC grade and city population size significantly predict the amount of excess noise in an area. However, HOLC grades had much greater effects on excess noise (AICc = 657.3, R² = 0.24, see Supplementary 1, Table S1 for full model output). These results indicate that historic HOLC grades are predictive of area-corrected excess noise in urban neighborhoods throughout the United States, being more correlated to excess noise than city area and population size. Excess noise was significantly lower in grade A neighborhoods compared to all other grades (p = < 0.001), and in grade B neighborhoods compared to grade D neighborhoods (Figure 1A). Grade C and D neighborhoods had the highest maximum noise overall (maximum noise values averaged across all cities per each HOLC grade: A = 77.3 dBA; B = 83.9 dBA; C = 88.0 dBA; and D = 89.8 dBA; Figure 1B). Notably, grade D neighborhoods experienced 17% higher maximum noise levels (12.5 dB - a more than 10-fold increase in sound pressure level) than grade A neighborhoods (Figures 1B). The distribution of maximum noise levels also varied by HOLC grade, where cities more frequently had maximum noise levels below the 70 dB EPA upper noise limit in grade A neighborhoods and nearly all cities had noise levels above the 70 dB limit in grade D neighborhoods (Figure 1C). Cities also more frequently had maximum noise levels in grade C and D neighborhoods above the 90 dB level (known to cause hearing loss, physical pain, and stress in humans ^61,62), relative to A and B neighborhoods (Figure 1C). These trends are also represented in spatial maps of noise distribution across cities, where C and D grade neighborhoods, relative to A and B grade neighborhoods, have a larger area covered by noise emitted from transportation networks on average over a 24-hr period, especially noise levels over 100 dBA (Figure 2). Combined, these results illustrate how the legacy of redlining, among other racially-targeted practices, continues to have subsequent effects on the prevalence of noise pollution in U.S. cities.

Impacts of noise to urban wildlife

We only included noise level data in the literature review when the original authors found significant biological responses to urban noise (i.e., noise emitted in urban environments; Supplementary 1, Figure S1). Therefore, for the remainder of this paper, we refer to any statistically significant response to urban noise found within these original studies as “responses”. Most studies of noise impacts on urban wildlife occurred in North America, followed by Europe (Supplementary 1, Figure S2), highlighting a bias in the distribution of urban noise studies, with most research conducted in the Global North as compared to the Global South. Additional biases found in urban noise research include responses to noise (‘biological response’), categories and sources of noise, and groups of taxa. Vocal behavior was the most studied biological response to noise (Figure 3A), followed distantly by population-level responses and physiological responses. Birds were the most frequently studied taxa (comprising 84% of papers in our review), but we observed an increase in the number of studies on other taxa in recent years (Figure 3B). Environmental and transportation noise (where the acoustic energy is predominantly focused in frequencies < 2Khz) were the most studied noise categories, and the effects of these two chronic noise categories on vocal behavior made up the majority of studies that we reviewed (Figure 3C). Only four aquatic studies were included in our review, demonstrating a strong bias towards terrestrial research in urban environments. Therefore, we restricted our analysis to only the terrestrial studies.

The average maximum, mean, and minimum noise levels that affected wildlife across studies in this review ranged from 45-113 dB, 32-112 dB, and 23-86 dB, respectively (Figure 4A). Only 15% of studies in our review documented a biological response below a mean noise level of 50 dB (Figure 4A), the approximate average noise level found across all HOLC grades (Figure 1B). However, the percentage of studies that found a response increased rapidly until noise levels exceeded 90 dB (Figure 4A). Redlined communities experience greater spatial coverage of noise levels at 90 dB and above (Figure 1 and Figure 2), at which 95% of studies found a biological response. Thus, as noise increases to the maximum levels found more commonly in redlined neighborhoods, the cumulative evidence of biological effects on wildlife also likely increases.

Diverse biological responses to urban noise exposure were documented for multiple taxa and trophic levels across the range of minimum, mean, and maximum noise levels recorded in studies (Figure 4B, 4C, and 4D). Urban noise ranging from 23-113 dB was associated with changes at the ecosystem (multiple species or community-level responses), population, and species level, including changes to animal physiology, fitness, and multiple behaviors (i.e., vocalization, vigilance, movement, mating, and foraging; Figure 4B), with physiological responses being exhibited over the widest range of noise levels. Noise levels below 50 dB, less than the recommended noise exposure known to cause harm in humans by the EPA (EPA 2018), still elicited changes in vocalization, population metrics, physiology, fitness, and whole ecosystem metrics (Figure 4B). Multiple bird studies found evidence that noise levels ranging from 23 to 93 dB were associated with changes to abundance and richness, community composition, physiology, reproduction, mating behaviors, vocalization characteristics, vigilance, and foraging behaviors (Figures 4B and 4D). Studies of terrestrial mammals found changes in abundance and vocal, vigilance, and foraging behaviors at noise levels between 38 and 80 dB (Figures 4B and 4D). Noise levels between 37 and 78 dB altered the abundance, vocal, movement, and mating behavior of herpetofauna, and noise from 68 to 116 dB altered the reproduction, and vocal and vigilance behaviors of terrestrial vertebrates (Figures 4B and 4D).

This study aimed to advance knowledge in urban ecology through a justice-centered approach that explicitly considers how racially-targeted zoning practices, like redlining, can shape the distribution of noise and potential impacts on wildlife. We found 1) strong evidence that noise is inequitably distributed across HOLC redlining grades in 83 U.S. cities; 2) that environmental, transportation, and industrial noise drive shifts in diverse biological responses (including population- and ecosystem-level, physiological, fitness, and behavioral responses) across a broad range of urban taxa; and 3) that the cumulative evidence of biological effects on urban wildlife increases as noise exposure rises until reaching levels over 100 db. Below, we discuss these findings and their importance to urban and acoustic ecology and highlight a list of key future questions that integrate noise pollution, wildlife, and social inequity to advance knowledge relevant to urban conservation practitioners and planners.

Noise distribution across HOLC redlining grades

Our analysis of the distribution of noise in U.S. cities provides clear evidence that noise is not equitably distributed in cities with historical redlined communities. In our model, redlining had a stronger contribution in predicting noise pollution than other factors (i.e., population size). Grade D neighborhoods experienced a greater spatial extent of excess noise and more than a 10-fold increase in sound pressure level than grade A neighborhoods (Figures 1A, 1B, and Figure 2). Although not focused on redlining explicitly, other research has similarly found that noise exposure is greater in residentially segregated neighborhoods ⁴⁹, and in neighborhoods that have lower socio-economic status and/or higher percentages of racial and ethnic minority residents in the United States ^50,51,63,64, South Africa ⁶⁵, Hong Kong ⁶⁶, Canada ⁶⁷, the United Kingdom ⁶⁸, and Germany ⁶⁹. Our study expands these findings by providing evidence explicitly linking noise exposure to the racially-targeted urban practice of redlining.

These inequities have direct consequences for humans — on average an increase of 10 dB of noise above background sound levels equates to elevated human health risks and a 90% decrease in listening ability ⁵². Moreover, grade A neighborhoods in our study experienced average maximum noise levels closer to the U.S. Environmental Protection Agency’s recommended upper limit for annual average noise exposure at 70 dB (Figure 1B and 1C), the baseline level at which damaging health effects emerge. In contrast, redlined neighborhoods more frequently experienced average maximum noise levels above 90 dB, with greater coverage of maximum values up to 120 dB (Figure 1B, 1C, and Figure 2) — equivalent to the sound experienced when standing next to a chainsaw and above the human pain threshold ⁷⁰. Noise levels between 90 and 120 dB can cause damage to hearing, hearing loss, physical pain, and psychophysiological stress in humans ^61,62. Notably, these maximum noise levels represent the highest noise levels found in a HOLC grade averaged over a 24-hr period, indicating that some sections of grade D neighborhoods are experiencing noise levels that are both severe (over 90 dB) and chronic (consistent over a 24-hr period). Thus, it is perhaps unsurprising that evidence is now accumulating that links residential segregation, noise pollution, and human health disparities ⁵⁷.

Emerging research suggests that there is a strong correlation between urban systemic racism and environmental health, and understanding these interconnected processes is an urgent priority for urban conservation ⁶. Yet, studies addressing this need so far have focused on other forms of environmental injustice, such as inequitable air and water pollution and disparities in green space coverage ⁶. Our finding that noise pollution is also related to systemic racism can inform urban planning. If noise is an important unseen factor shaping urban environments, then urban planning projects failing to take noise into account while addressing other environmental injustices in historically redlined communities may fall short of realizing their full beneficial potential. Our findings establish new research avenues to determine how inequitable noise pollution interacts with other forms of environmental injustice to exacerbate their impacts on marginalized communities and their wildlife neighbors. For example, noise, air, and light pollution often co-occur because they are both emitted from transportation and industrial sources. Yet, these forms of pollution are often only moderately correlated ^71,72, suggesting that noise impacts may extend beyond the footprint of other forms of environmental injustice.

Impacts of noise to urban wildlife

Our literature review demonstrates the widespread effects of urban noise exposure across species, behavior, demography, and environments. Urban transportation, environmental, and industrial noise were associated with changes to animal physiology, fitness, and multiple behaviors for all trophic levels and taxonomic groups (Figures 4B, 4C, and 4D). We found that noise levels as low as 23 dB can affect wildlife and that the cumulative effects of noise become more pronounced with higher noise levels (Figure 4A). Over 95% of studies (across multiple taxonomic groups; Figure 4D) found a biological response at 90 dB, a noise level more commonly observed in redlined communities. These findings reinforce those of Shannon et al. (2016)⁴³, who found that the cumulative effects of noise increased with noise level, with over 95% of terrestrial studies documenting a biological response at 90 dB as well (though not focused exclusively on urban noise). Thus, evidence consistently suggests that, as noise increases, the effects on wildlife become more widespread. Consequently, higher noise levels found in redlined neighborhoods may have substantially greater biological effects as more species respond with a broader range of biological shifts at such levels.

A majority of responses involved reduced biodiversity and altered acoustic diversity. Seventy-two percent of population-level studies reported reduced abundance or occurrence of wildlife with elevated noise exposure, and 93% of vocalization studies reported altered vocal behavior. Urban species, especially birds, often rely on acoustic communication to attract mates, defend territories, and signal dangers ⁷³. Urban noise frequently masks these vital signals, especially at lower frequencies ^74,75. This masking effect can cause taxa to alter their vocal behavior in multiple ways, including shifting the frequency of their song or increasing vocal amplitudes ^76–78, or altering the timing or complexity of their vocalizations ^79,80. When a species cannot adjust its acoustic signal, it may instead respond to noise by altering its movement or habitat use to avoid noisy areas ^81,82.

Synthesizing socio-ecological Impacts

The finding that higher noise is related to reduced biodiversity and altered acoustic diversity has notable implications for urban wildlife, humans, and equitable urban planning. Human residents in redlined neighborhoods have disproportionately lower access to parks and green spaces ^17,30. However, even when these neighborhoods do include more green spaces, they may still experience lower animal biodiversity and degraded natural soundscapes. As access to biodiversity and natural soundscapes has been shown to improve human health ⁵⁸, residents of redlined neighborhoods are likely experiencing both direct health impacts from inequitable noise ⁴² and indirect impacts from reduced access to the benefits from nature. Moreover, reduced biodiversity is often associated with reduced ecosystem function, resilience, and services for humans ^83,84. Additionally, the inequitable distribution of noise in urban landscapes could limit conservation funding and opportunities for people in redlined communities, as conservation projects tend to concentrate in areas of high biodiversity ⁸⁵. This highlights the intertwined need to address inequity in urban systems, for the benefit of people and wildlife.

Several cities across the United States are currently developing or implementing plans to address environmental justice issues by increasing access to parks and green spaces for underserved populations ⁸⁶, who are often within historical redlining boundaries ^30,59. For example, Denver voters approved a 0.25% sales tax increase to advance Denver’s Game Plan for a Healthy City ⁸⁷, which is working to provide equitable access to parks by identifying neighborhoods in greatest need of new or improved parks and green infrastructure. Similarly, Pittsburgh Parks and Recreation paired a tax referendum with data to improve park equity, along with New York, San Francisco, Philadelphia, Detroit, and Minneapolis, amongst others, using equity data to develop plans to increase equitable park access ⁸⁶. Importantly, many of these initiatives are being developed with direct input from local residents and neighborhood organizations, with a goal of using affordable housing agreements or other tools to avoid green gentrification (the process in which improving green infrastructure increases local property values, displacing lower-income residents; ⁸⁸. Yet, if new green infrastructure is added or improved without also mitigating noise, such parks and green spaces may still experience limited biodiversity and fewer opportunities for residents to encounter the beneficial effects of natural soundscapes. Thus, equitable urban planning projects should consider including noise mitigation approaches to ensure that both wildlife and people receive the benefits of additional green infrastructure without the degrading effects of noise. Such mitigation measures may include adding physical barriers to limit noise emission from industrial and construction zones, creating tree lines and border vegetation of specific heights and densities to reduce noise transmission, implementing traffic speed reductions in areas near green spaces, and applying technological improvements to reduce noise emitted from tires and road surface substrates (see the review of mitigation methods and associated references cited in Table 4 from Shannon et al. (2016)⁴³).

Study Scope

While our results highlight important consequences of inequitable noise for wildlife and humans, there are certain limitations to the scope of our literature review that should be considered when interpreting our results. The focus of research on urban noise impacts to wildlife was biased across taxonomic groups, biological responses, and geographic locations studied. Our results show that the vast majority of studies on noise impacts to urban wildlife are being conducted on birds, which is likely driven by birds and bird vocalizations being more easily observed and measured than many other groups and biological responses in the urban environment. Invertebrates were the least studied taxa, as has been found by other reviews, despite invertebrates contributing disproportionately to global biodiversity ⁸⁹. Among all taxonomic groups, changes in vocal behavior and population-level metrics were the most observed effects of noise (Figure 3A and 3C), and physiological responses were also more commonly studied in urban wildlife. We found a strong geographic bias in urban noise and wildlife research, with 74% of studies conducted in North America and Europe alone (Supplementary 1, Figure S2), which is only slightly smaller than the findings of 81% and 79.6% from Shannon et al. (2016)⁴³ and Jerem and Matthews (2021)⁹⁰, which covered impacts to wildlife beyond urban noise. It will be critical to broaden the taxonomic, biological, and geographic breadth of research on urban noise impacts to wildlife to better understand and predict how inequitable noise will impact rare species and species with unique life histories, auditory capabilities, and adaptive capacity to noise ⁴³. Another limitation of our review was the bias towards terrestrial ecosystems. The effects of noise on aquatic urban species (documented in only four studies that we reviewed) were far less explored than effects on terrestrial species. As a result, our review only considered impacts on terrestrial wildlife.

Further limitations that warrant consideration include the variation in noise metrics, study designs, and geographic and sampling biases represented across the studies included in our review. First, a variety of acoustic metrics with different frequency weighting and bandwidths were synthesized together in our review and analysis (Figure 4), as we were unable to adjust all values to a common acoustic metric that could be compared across studies (a lack of accurate reporting of acoustic metrics is a key concern noted by McKenna et al. 2016 ⁹¹). As a result, we have avoided making comparisons of how noise levels differentially affected taxonomic groups, trophic levels, or biological responses because researchers may have explored different noise levels for different groups, and thus any inter-group differences may be related to study design rather than noise levels. Given our urban focus, people and animals likely are exposed to chronic low frequency noise ⁹², suggesting that our findings can be more directly compared. However, the variation in metrics used across studies warrants caution in making such direct comparisons. We also caution against using our findings to conclude that low-decibel urban noise has no effect on wildlife. Although the cumulative effects on wildlife increase with noise, animals may still respond to very low noise levels ⁴³, and the lack of evidence of effects at lower noise levels may be partially driven by biases in study design, with fewer researchers choosing to study low noise exposure levels. Similarly, redlined neighborhoods are underrepresented in citizen science projects that are used to study urban ecology ⁹³, which likely explains why noise levels above 100 dB - more common to redlined neighborhoods - are not well represented in the urban acoustic ecology literature that we reviewed (Figure 4A). Thus, our findings likely underestimate the full impact of inequitable noise on urban wildlife and future research should prioritize evaluating noise impacts to wildlife at levels of 100 dB and above.

Our use of Web of Science for the literature review also likely missed relevant publications in the non-peer-reviewed gray literature and government reports ⁹⁴, which likely contributed to the lack of publications in our review from the Global South. However, a recent review that used a more comprehensive approach similarly found evidence that the Global South was underrepresented in research on noise impacts to wildlife ⁹⁰. These geographic biases arise, in part, from disparities in conservation research and funding, where scientists from wealthy countries in the Global North frequently conduct research in the Global South without effectively engaging local communities ⁹⁵. This process, known as parachute science, disconnects local and Indigenous peoples from leading their own environmental initiatives and instead gives credit for and authority over conservation outcomes to institutions in the Global North ⁹⁵. Increasing capacity for locally led research on noise impacts to wildlife across geographies not well represented in the Global South will be of critical importance, as many of these regions support greater global species richness and functional diversity ^96,97, and are undergoing rapid urbanization ⁹⁸. Likewise, an increasing proportion of the global population are living in urban areas ⁹⁸, which necessitates radical changes in planning for sustainable and 'healthy' cities of the future.

While previous research reveals similar trends in the effects of noise on terrestrial wildlife, ^51,97 our review explicitly focuses on the impacts of noise in the urban environment and reinforces connections between social inequities and wildlife outcomes. Chronic and inescapable noise is only one of many environmental pollutants that may affect urban wildlife, and future studies should investigate how noise interacts with other factors, such as light and chemical pollution. While the effects of the unique, complex, and pervasive soundscapes of urban environments on people are somewhat understood, our review demonstrates that large gaps exist in our knowledge of how noise shapes urban wildlife. Addressing such gaps will broaden our understanding of complex urban socio-ecological systems.

Future directions for studying urban noise pollution

We collected data on the inequities of noise pollution within cities as well as the diversity of impacts that noise pollution may have on urban wildlife. With this information, we can begin to make predictions about the impacts of inequitable noise on ecological systems. Previous research has already illustrated that increased impervious surface cover and decreased tree cover is associated with systemic racism and can impact evolutionary outcomes through alterations of gene flow, disease, etc. ^6,99,100. Now, we can add noise pollution to the growing list of ecological impacts driven by systemic racism. This insight provides a foundation for new avenues of research on the social and ecological consequences of inequitable noise pollution and solutions for urban communities. Here we use our findings to outline outstanding questions that can address some key knowledge gaps on the impacts of inequitable noise for urban wildlife, people, and human-wildlife relationships.

How does noise pollution or the presence of natural sounds interact with tree cover, building density, and other environmental gradients that are inequitably distributed across cities to alter wildlife distributions and population connectivity?
How does inequitable noise and inequitable natural soundscape exposure affect human health and well-being? Is legislation effectively tackling the health impacts of noise in urban environments?
How does exposure to inequitable noise pollution affect community perceptions of wildlife and human-wildlife relationships? How might these perceptions affect urban wildlife management and conservation priorities?
What mitigation techniques, such as noise barriers or green walls, and infrastructure improvements (e.g., building spatial orientation and green space) yield the most benefits for urban wildlife and people in areas of higher noise pollution?
Do elevated noise levels drive shifts in acoustic traits of urban species populations? How might these shifts in traits vary spatially and temporally (rate of change), and how might these drive evolutionary outcomes and fitness consequences?
Do established hypotheses (e.g., acoustic adaptation hypothesis, Lombard effect, luxury effect hypothesis) accurately predict the sensitivity or tolerance potential of urban species in light of inequitable noise? Does inequitable noise contribute to or exacerbate these hypotheses?
Does inequitable noise have cascading consequences for ecosystem function, ecosystem resilience, and the ecological services provided to humans in urban environments?

Our study combined a comprehensive synthesis of the impacts of socially driven inequitable noise pollution with data on the effects of urban noise on wildlife. We found evidence that noise is inequitably distributed in cities across the United States, and that inequitable noise may drive complex biological responses across a diversity of urban wildlife. This knowledge focuses attention on inequitable noise pollution as an underappreciated driver of heterogeneity in patterns of urban biodiversity, highlighting the need for further research at the intersection of noise, environmental justice, and ecology. Such knowledge can be leveraged by urban ecologists, acoustic ecologists, social scientists, and urban planners interested in understanding how social processes, like redlining, can alter ecological properties that then feedback to influence human societies and human-wildlife relationships. Urban ecologists are being called to reimagine a more socially just vision of conservation science and practice that centers racial and environmental justice to drive holistic and equitable policy changes in cities ⁶.Here we lay a foundation for future research that advances acoustic and urban ecology by centering equity and challenging systems of oppression that remain embedded in our city infrastructure.

1. Spatial Analysis of Urban Noise Pollution

We conducted a spatial analysis of the distribution of noise pollution across HOLC grades for 83 cities in the United States (Supplementary 1, Table S2). To be included in the study, the city needed to be included in both datasets used in the analysis: 1) the Mapping Inequality Project dataset on the distribution of HOLC grades across cities ¹⁰¹, and 2) the U.S. Department of Transportation, National Transportation Noise Map 2018 ⁶⁰. Any cities in which the distribution of HOLC grades did not include all four grades (A-D) were excluded from the analysis, which largely excluded cities with population sizes below 100,000 people.

To evaluate noise exposure across HOLC grades for each city in our study, we acquired spatial data on the distribution of HOLC grades across U.S. cities from the Mapping Inequality Project¹⁰¹. We also acquired data on road, rail, and aircraft noise (hereafter transportation noise models), from the U.S. Department of Transportation, National Transportation Noise Map 2018,⁶⁰ which has been used by other investigators to assess noise exposure in the United States ^50,102. The transportation noise models represent potential exposure to transportation noise reported on a decibel scale in a 30m x 30m pixel resolution. Here noise represents the average noise energy produced by road, rail, and aviation networks over a 24-hour period, measured in A-weighted decibels (dBA) (LAeq, 24h) at sampling locations deployed across a uniform grid in each city at an elevation of 1.5 m above ground level. Noise levels below 35 dBA are assumed to have minimal negative impacts to humans and the environment and thus are represented with null values in the transportation noise models.

For each HOLC grade and each city, we used zonal statistics in ArcGIS Desktop v. 10.7 to summarize the median noise levels and area covered by excess noise (i.e., values > 35 dBA). We used the resulting zonal statistics estimates and the formula from Collins et al. (2019)⁵⁰ to calculate an area-corrected measure of excess noise:

N = (r * Md)/a

where N is excess noise in each HOLC grade (with units of dBA/900m²); r is the area covered by the 30m x 30m pixels with noise values >35 dBA across all polygons of the same HOLC grade in each city; Md is the median transportation noise value (in dBA) for those same pixels; and a is the total area of all polygons of the same HOLC grade in each city. Thus, N represents a measure of both the level of noise and the area covered by excess noise in a given HOLC grade for each city. We used the N measure of excess noise as the dependent variable in the regression model of excess noise across cities described in the Statistical Analysis section. We also produced maps of excess noise and the distribution of HOLC grades for all cities included in our study.

2. Statistical Analysis

We built linear regression models using standard least squares to examine the relationship between noise exceedance (N), HOLC grade, and city population size. Prior to analyses, we explored our data to assure that the assumptions of this test were met. We constructed four separate models, each with noise exceedance as the response variable and one of the following as predictive variables: HOLC grade only, city population size only, HOLC grade + city population size, and the interaction of HOLC grade and city population size. We did not incorporate city area because the exceedance value N is an area-corrected metric. Following each model, we plotted residuals against the fitted values to determine if there was non-constant error variance. As our N-mean variable displayed non-normality, we performed a log transformation on the variable and reanalyzed models using the log-transformed data. Statistical analyses were done in R Studio 4.2.1¹⁰³. We considered models with the highest R²and the lowest AICc as the best predictors of noise.

3. Literature Review on the Impacts of Noise to Urban Wildlife

To assess the effects of noise on wildlife in urban environments, we conducted a literature review (Supplementary 1, Figure 1) using Thompson’s ISI Web of Science and adapting the methods of Shannon et al. (2016)⁴³. We adjusted of Shannon et al.’s search criteria to include urban phrases, resulting in the following search terms (TS=(WILDLIFE OR ANIMAL OR MAMMAL OR REPTILE OR AMPHIBIAN OR BIRD OR FISH OR INVERTEBRATE) AND TS=(NOISE OR SONAR) AND TS=(CITY OR *URBAN OR METROPOLITAN)). We only selected papers published between 1990 and 23 June 2021 (i.e., the date we conducted our search) within the ISI Web of Science categories of ‘Acoustics’, ‘Zoology’, ‘Ecology’, ‘Environmental Sciences’, ‘Ornithology’, ‘Biodiversity Conservation’, ‘Evolutionary Biology’, and ‘Marine Freshwater Biology’. This returned 691 peer-reviewed papers, which we filtered so only empirical studies focused on documenting the effects of anthropogenic noise on wildlife in urban or suburban ecosystems or the effects of urban noise on wildlife in rural environments were included in the final data set (n = 207). We excluded reviews, meta-analyses, methods papers, and research that took place outside of urban or suburban areas where the noise was not explicitly denoted as urban (e.g., omitted studies that measured traffic noise by parks and reserves in rural areas).

For the 241 articles previously analyzed in of Shannon et al. (2016)⁴³, one of our authors reviewed each paper to determine which studies were focused on urban noise (n = 46). We then verified whether there were significant biological responses to a particular noise level threshold, noting each noise level if multiple biological responses were recorded. We recorded responses to noise into one of eight possible biological response categories, many of which were taken or modified from the biological response categories utilized in Shannon et al. (2016)⁴³. The following were the biological response categorical values: movement behavior, vocal behavior, physiological, population, mating behavior, foraging behavior, vigilance behavior, life history / reproduction, and ecosystem. Further definitions and descriptions for each biological response category may be found in the supplemental information (S3). For any new articles published since the Shannon et al. (2016)⁴³ dataset (n = 354) or those published between 1990 and 2013 but not reviewed by Shannon et al. (n = 96), two of our authors reviewed each paper to first determine which studies met our criteria (n = 161) and then compiled data on a number of variables of interest, including the noise levels and their resulting biological responses that were statistically significant (Supplementary 1, Table S3). For this subset of papers, one author was randomly assigned a list of papers and then a second author was randomly assigned to assess the accuracy of the data collected by the first author. Any discrepancies were discussed as a group until an agreement was reached.

Noise categories (environmental, transportation, industrial, multiple, other) were chosen for each paper by noting the explicitly stated source or description of urban noise described in the methodology. Noise levels and their units were reported for each paper, with only noise levels reported in decibels (dB) being used in data analysis. All terrestrial papers used a reference pressure of 20 microPascals (μPa). Due to the low sample size of aquatic studies (n = 4), differences in reference pressures, and varying sound intensities amongst aquatic studies, we only included terrestrial studies in statistical analyses and figures. We recorded the sound metric used (i.e., SPL, SPL Max, Leq) for each paper, but were unable to convert the various sound metrics given to a single sound metric for standardization during analysis. Thus, there were various sound metrics used in the analysis of the data extracted from the literature search, in particular for the cumulative weight-of-evidence curve, which poses a limitation in the comparison of noise levels amongst papers. Additionally, we recorded the weightings for each noise level, with many of the papers being A-weighted (dBA; n = 100) and Z-weighted (dBZ; n = 4). These weightings relate to typical characteristics of sounds as observed by humans. Many papers, however, did not record the weighting and/or the exact sound metric used, leading to some unavoidable uncertainty in the comparison of noise measurements. We used the extracted noise levels to develop a cumulative weight-of-evidence curve as a function of the noise level at which a biological response was documented. This curve summarizes the cumulative percent of studies that reported a biological response at or below a given noise level across a wide range of taxa, biological responses, and acoustic metrics, with some taxa, responses, and metrics being more represented than others.

Author Contributions: T.M.L. and S.P.B. conceived the initial study. J.R.N.-O., T.J.L., E.A., A.L., M.L., T.M.L., K.A.S., S.S., A.K.V., and S.P.B. collected review data, participated in data analyses, and wrote the initial draft of the article. K.A.S. and S.P.B. conducted spatial analyses and modeling approaches. All authors contributed to revisions.

Competing Interest Statement: The authors declare that they have no known competing interests.

Acknowledgments

We thank Megan McKenna for providing feedback on an early draft of the manuscript. This research was funded by Colorado State University. J.R.N.-O., T.J.L., E.A., and M.L. were supported by NSF Graduate Research Fellowships. K.A.S. was supported by an American Association of University Women Fellowship.

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It's not just noise: The consequences of inequitable noise for urban wildlife

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Abstract

Figures

Introduction

Results

Noise distribution across HOLC redlining grades

Discussion

Noise distribution across HOLC redlining grades

Impacts of noise to urban wildlife

Synthesizing socio-ecological Impacts

Study Scope

Future directions for studying urban noise pollution

Conclusion

Materials And Methods

1. Spatial Analysis of Urban Noise Pollution

2. Statistical Analysis

3. Literature Review on the Impacts of Noise to Urban Wildlife

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References

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