Environmental contamination predicts mammal diversity and mesocarnivore activity in the Seattle- Tacoma metro area

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Estien, Zachary Hawn, Mark J. Jordan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6229535/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jun, 2025 Read the published version in Urban Ecosystems → Version 1 posted 24 You are reading this latest preprint version Abstract Environmental factors controlling the distribution and abundance of wildlife populations in the Anthropocene are increasingly complicated by historical and ongoing industrialization. The legacy of industrialization has enduring impacts on environmental quality, with downstream consequences for wildlife. However, industrial contaminants are not evenly distributed across or within cities, and their effects on free-ranging wildlife at the population and community levels remain poorly understood. We investigated whether environmental contamination risk from industrial pollutants was associated with mammalian diversity and carnivore activity in the Seattle-Tacoma metropolitan area, Washington, USA, a historically industrialized region. Using camera trap data collected across 78 sites, we modeled environmental contamination risk, natural land cover, and human population density against several mammal community metrics and against activity rates of carnivore species. We found that mammalian diversity and evenness decreased as contamination risk increased, especially in Seattle. Across the metro area and within Seattle, coyote activity was negatively associated with contamination risk, while raccoon activity was positively associated with contamination risk; opossums showed no response. Within Tacoma, contaminant risk was not significantly associated with mammal community metrics or carnivore activity, but human population density had a negative influence on coyote activity and a positive influence on opossum activity. Our results highlight the impacts of industrialization in ecological processes, and the need for species- and city-specific approaches in understanding the role they play in shaping urban wildlife communities. Ecological studies that incorporate these impacts can inform urban planning and conservation strategies that improve environmental quality for urban wildlife populations. urban ecology camera traps pollution environmental contamination mesocarnivores multi-city Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 INTRODUCTION Cities are complex landscapes where human and natural systems are intimately coupled, and are characterized by high variation in environmental quality for human and nonhuman life (Collins et al. 2000, Ramalho and Hobbs 2012, McPhearson et al. 2016, Keeler et al. 2019, Des Roches et al. 2021). Within and across urban areas, the quality of habitats and environments varies greatly across and within cities (Cushing et al. 2015, Estien et al. 2024b), influenced by factors such as green space availability; pollution sources such as industrial facilities and wastewater; and human population density. Environmental quality, or the health of an environment as defined by environmental characteristics that impact living beings, plays a large role in dictating the success of human and wildlife populations and communities in cities (Collins et al. 2000, Saaristo et al. 2018, Murray et al. 2019, Lev et al. 2020, Fatima et al. 2023). In addition to factors such as competition, mating opportunities, and availability of food and shelter, urban wildlife must attenuate their responses directly to environmental quality (e.g. heterogeneity in noise pollution, contaminated water sources, green space size and complexity, etc.). For example, increased anxiety in birds in response to pollution exposure can suppress distress vocalizations (Yamane et al. 2007). Environmental quality in cities is heavily impacted by various types of pollution (i.e. environmental contamination, (Agyeman et al. 2016), which can result in deleterious health effects or even lethal consequences in both wildlife and humans (Dominoni et al. 2016, Birnie-Gauvin et al. 2016, Sanders and Gaston 2018, Kunc and Schmidt 2019, Kok et al. 2023). Contaminant exposure in free-ranging wildlife is associated with parasitic disease outbreaks (Murray et al. 2016, Serieys et al. 2018), behavioral differences (Brodin et al. 2013, Flahr et al. 2015, Tüzün et al. 2021), gut dysbiosis (Rosenfeld 2017), reduced fertility (Somers 2011), and endocrine disruption (Guillette 2000). Extensive in situ experimental research exists on the impacts of pollution in mammals, wherein animals are placed in “exposure facilities” and exposed to environmental contamination, acting as sentinels for the impacts of contamination (Somers 2011). For example, one study drew air from a tunnel into a laboratory vivarium to expose study animals to air pollution from a freeway tunnel (Patten et al. 2021). Work using this methodology has uncovered evidence of genetic mutations (Yauk et al. 2000), altered behavior (Saaristo et al. 2018), and physiological imbalances (Guo et al. 2020). Recently, studies on wild mammals as sentinels of environmental contamination wherein free-ranging mammals live in contaminated environments have allowed researchers to assess population-wide effects (Wainstein et al. 2022). Individual-level impacts on physiology and behavior may further scale up to affect community-level processes, such as competition and predation, by altering the presence and distribution of populations (Saaristo et al. 2018). However, it is difficult to determine population- and community-level effects in an experimental setting. Therefore, knowledge on the consequences of contamination on urban-adapted mammals, and subsequent impact on population and community dynamics, remains limited. While spatial patterns of environmental quality vary widely within and among cities, prior work investigating the social-ecological factors contributing to those patterns provide leverage to examine how gradients of quality impact urban wildlife populations (Cushing et al. 2015, Rigolon 2016, Lane et al. 2022, Estien et al. 2024b). Within urban mammal communities, carnivores are uniquely vulnerable to the health impacts of pollutants due to biomagnification, the exacerbation of contaminant exposure in higher trophic levels (Rodríguez-Jorquera et al. 2017). However, due to their large home range requirements, lower population densities, and sensitivity to human activity, large carnivores such as mountain lions ( Puma concolor ) and gray wolves ( Canis lupus ) are less common within urban regions. Conversely, mesocarnivores have been highly successful in urban areas due to dietary and behavioral flexibilities that facilitate persistence in novel or disturbed environmental contexts (Bateman and Fleming 2012, Caspi et al. 2022). However, these traits can also expose urban mesocarnivores to unique environmental risks (Murray et al. 2016, Richards et al. 2018, Serieys et al. 2018, Shakouri and Gheytasi 2018). The impact of environmental contamination may be more apparent in synanthropic mesocarnivores such as coyotes ( Canis latrans ) and raccoons ( Procyon lotor ) due to their better ability to exploit and persist in urban environments compared to larger carnivores, which may avoid highly contaminated areas entirely due to their overlap with human activity (Marneweck et al. 2022). Due to their success across urbanization gradients, mesocarnivores may act as sentinels for environmental contamination caused by industrial activities (Hernández et al. 2017, Brand et al. 2020, Guo et al. 2020, Aeluro and Kavanagh 2021). For example, the Green-Duwamish River, a waterway in Washington state, USA, begins in the Cascades mountain range but passes through a highly industrialized region dominated by historic Superfund sites before finally emptying into the Puget Sound in the industrial district of the city of Seattle. Along this tributary, river otters living closer to Superfund sites had increased polychlorinated biphenyls (PCB) concentrations in their scat (Wainstein et al. 2022). In Seattle and its neighboring city Tacoma, dense human populations exist in conjunction with large-scale shipping and industrial activity. Both Seattle and Tacoma are economically significant port cities and are throughways of major interstates. Further, the region (known as the Seattle-Tacoma metropolitan area) hosts an abundance of environmental contaminant sources, such as National Priorities List sites (a.k.a. Superfund sites; sites defined by the U.S. Environmental Protection Agency as containing hazardous substances that pose risks to human health and the environment due to industrial pollution, improper waste disposal, or chemical spills [Mariner et al. 1997, Abel and Stephan 2017]), historic smelting operations, (Ajax and Meyer 1987), steel plants (Abel and White 2011, Sprague 2015), hazardous waste sites (e.g. asphalt facilities; (Abel and Stephan 2017), and airplane plants (Abel and White 2011). As in many U.S. cities and indeed, across the globe, the burden of pollution in the Seattle-Tacoma region often falls on communities of color and working-class communities (Bassok et al. 2010, Kondo et al. 2022, Bramble et al. 2023). Pollutants linked to these contaminant sources include PCBs (Vorhees et al. 1999), polycyclic aromatic hydrocarbons (PAH) (Brenner et al. 2002), heavy metals (Mariner et al. 1997), and particulate matter (Abel and White 2011). The legacy of such industrial and waste-producing sites is also long-standing; even in cases where they are removed and rebuilt into residential areas, contaminants still persist in the environment (Abel and Stephan 2017, Bramble et al. 2023). Consequently, wildlife communities in this region are likely experiencing variable contaminated landscapes, with potential consequences for biodiversity. Still, the region hosts a vibrant community of wildlife common to western North America. The Seattle-Tacoma metropolitan area is therefore a fitting case study for exploring the impacts of environmental contaminants on wildlife. The primary objective of this study was to determine the impacts of environmental contamination on wildlife activity in the Seattle-Tacoma metropolitan region, Washington, USA, at two taxonomic scales: first, the medium-to-large mammal community, and second, for specific carnivore species. Our secondary objective was to determine the relative impact of environmental contamination risk, as defined by a previous study on environmental contamination across Washington census tracts (Min et al. 2021, relative to commonly-analyzed spatial covariates known to be affiliated with urban wildlife activity: proportion of natural land cover and human population density. We predicted that areas with higher proportion of natural land cover and lower environmental contamination risk would have greater relative mammalian diversity, richness, and evenness compared to sites with less natural land cover and those with higher environmental contamination risk. We also predicted higher detection rates of carnivore species in areas with higher proportion of natural land cover and lower contaminant risk. Within this pattern, we expected mesocarnivores to respond more strongly to contaminant risk; conversely, we expected larger carnivores to respond more strongly to proportion of natural land cover and human population density. To address our objectives, we analyzed camera trap data with generalized linear mixed models (GLMMs) to determine whether three spatial covariates – environmental contamination risk, human population density, and proportion of natural land cover – were associated with a) mammalian community metrics (diversity, richness, and evenness) and b) detection rates (i.e., activity) of carnivores. Our mammal community included 17 species ranging from rodents to ungulates to carnivores (Table 1 ). We also analyzed these relationships for 9 individual carnivore species in our study area (Table 1 ), but due to data limitations, our results focus on 3 common mesocarnivore species in our study area: raccoons, coyotes, and Virginia opossums ( Didelphis virginiana ). Raccoons are the quintessential synanthrope, able to exploit anthropogenic resources amidst urban habitat fragmentation (Graser et al. 2012). Coyotes are equally successful urban exploiters that have witnessed rapid geographic expansion, establishing populations in a cadre of biomes and ecosystems throughout the North American continent (Grigione et al. 2014, Murray et al. 2016, Sugden et al. 2020). Lastly, while they are taxonomically marsupials, Virginia opossums (hereafter opossums) occupy similar trophic positions to sympatric mesocarnivores, and are persistent across urbanization gradients in North American cities (Wang et al. 2015, Worsley-Tonks et al. 2020, Buckley et al. 2024). METHODS Study area and design Cameras were deployed as part of the Seattle Urban Carnivore Project (Seattle, Washington), and the Grit City Carnivore Project (Tacoma, Washington) (total n = 33 cameras and n = 41 cameras, respectively; Fig. 1). These cameras were deployed along urban-exurban gradients following protocols from the Urban Wildlife Information Network (UWIN) camera study design protocol, with cameras placed in parks along multiple transects at least 1 km apart along the designated gradient (Haight et al. 2023, Fidino et al. 2024). Camera transects spanned the cities of Seattle and Tacoma as well as surrounding cities and towns in King and Pierce Counties, including Bothell, Redmond, Tukwila, Renton, Auburn, Puyallup, and Eatonville (Fig. 1). During each season (spring, summer, fall, winter), cameras were deployed for a minimum of 28 consecutive days, barring theft, vandalism, or camera malfunction. Generally, cameras were deployed in April, July, October, and January, and removed from the field after a four-week total deployment period. Cameras were deployed for a total of 5 seasons (January 2019-January 2020). The UWIN study design protocol during this time period included deployment of fatty acid scent discs paired with each camera trap, which have been found to have no impact on mammal detection rates (Fidino et al. 2020). Images were identified to the species level by trained staff, students, and volunteers. Spatial covariates All spatial analyses were done in the R computing software. For our natural land cover covariate, we calculated proportions for each land cover type using data from the National Land Cover Database. We then combined all natural land cover types to create a final proportion of natural land cover for each 30m raster pixel. In our study area, these cover types include various forest, shrubland, herbaceous, wetland, and cultivated land classes (https://mrlc.gov; Haight et al. 2023). For human population density, we used openly available data from the University of Wisconsin (https://silvis.forest.wisc.edu/data/housing-block-change/); these layers were calculated at the census block level using U.S. census data from 2020. Data from census blocks in our study area were then rasterized using the R packages sf and terra . Values of both natural land cover and human population density were averaged around each camera site in a 1km buffer using the R package terra (Gehrt et al. 2009, Magle et al. 2016). To quantify environmental contamination risk, we used data from The Washington Environmental Health Disparities Map, which developed publicly available indices estimating cumulative impacts of environmental pollution burden experienced in Washington communities (Min et al. 2019, 2021); https://fortress.wa.gov/doh/wtnibl/WTNIBL/). The Washington Health Disparities Map and similar datasets (e.g. CalEnviroScreen in California) are widely used in urban planning, human health, and ecological studies (Cushing et al. 2015, Nardone et al. 2020, Min et al. 2021, Estien et al. 2024b). In this map, pollution burden is divided into environmental exposures, which are direct measurements of pollutants (i.e. toxic releases from industrial facilities, particulate matter 2.5 (PM 2.5 ) concentrations, ozone concentrations, and diesel exhaust emissions); and environmental effects (i.e. lead risk from housing, proximity to hazardous waste treatment storage and disposal facilities, proximity to national priorities list facilities [Superfund sites], and proximity to risk management plan facilities), which represent adverse environmental qualities that are not direct exposures, but still pose potential health risks. We created two composite variables: “environmental exposures” and “contamination risk” (i.e. environmental effects, which we renamed for clarity). To create these variables, we used methods from Min et al. (2021), which quantified projected pollution burden in human communities. In brief, values of each index were downloaded from the Washington Environmental Health Disparities Map website and converted to spatial data in R using census tract GEOIDs. Each individual index was calculated at a 1 km buffer around each camera trap site using the R packages terra . Indices were then averaged within their group to create the final composite “environmental exposure” and “contamination risk” composite variables for the entire dataset. We excluded one variable from Min et al. from our contamination risk composite variable – wastewater discharge from treatment plants – because data was missing for over half of our census tracts. We then scaled each composite variable based on values at all study sites, such that a score of 0 represents no burden, and a score of 1 represents the highest burden. This scaling was done separately for the metro-area dataset and each city-specific dataset. Each variable therefore represents the average exposure or risk experienced around the site in relation to other sites in the dataset to be analyzed. We found that, at our study sites, environmental exposure and contamination risk were relatively strongly correlated (R = 0.68, Fig. 4). We therefore chose to proceed with only contamination risk, because it approximates proximity to landscape features (i.e. industrial facilities causing contamination) that have a direct influence on habitat quality. Prior to running models, we assessed variance inflation factors for these and all final variables using the regclass package in R, which suggested low multicollinearity (VIF < 2 [Petrie 2020]). Data analysis We used generalized linear mixed models (GLMMs) to evaluate the effects of our spatial covariates on mammal community diversity and carnivore species activity. Analyses were restricted to sites with 3 or more active seasons and at least 18 active camera-trap nights per season (Magle et al. 2016). Photos within 30 minutes of each other were removed to ensure temporal independence between detections (Guo et al. 2017). For the mammal community diversity model, we removed all non-mammals as well as rodents smaller than squirrels from the dataset, as consistent capture of small mammals and other wildlife taxa (e.g. birds, herpetofauna) with camera traps is unreliable. We also removed domestic dogs, cats, and humans. Using our GLMM framework, we modeled both mammal community diversity and detection rates of carnivores within and across the two focal cities. We modeled the two cities together (“metro-area model”) as well as separately to investigate how our selected spatial covariates were affiliated with our response variables both across the metro area and within each focal city transect. We used detection rates as a proxy for activity at each site (often referred to as relative abundance) (Lovell et al. 2022, Sievert et al. 2023). We used the glmmTMB package in R to fit our models (Avrin et al. 2021). We included season as a variable due to its potential role in variation of wildlife activity throughout the year (e.g. dispersal, mating seasons). To account for non-independence among seasons sampled at the same station, site was included as a random effect in all models. We scaled all continuous variables with a mean value of 0 and standard deviation of 1 within the model. GLMMs were built for each species of interest, with species detections per site per season as the response variable (Lovell et al. 2022, Sievert et al. 2023). We determined that the negative binomial distribution was the best-fitting discrete distribution for each species using the car and MASS packages in R (Ripley et al. 2013, Gorjanc et al. 2024). Focal city, contamination risk, natural land cover, human population density, and season were included as fixed effects in the “metro-area” model. In individual city models, all variables except focal city were included. We used an offset on the log scale to account for variation in the number of sampling occasions at each site (i.e., the number of days the camera was operational during a given season). Model diagnostics were performed using the dharma package in R (Hartig and Hartig 2022). To model mammal community diversity, we calculated Shannon’s diversity index, species richness, and Pielou’s evenness for the mammal community at each site/season combination using the vegan package in R (Oksanen et al. 2013). We then averaged these values across seasons. A GLMM was built and fit to a Gaussian distribution for Shannon’s diversity and Pielou’s evenness, and a Poisson distribution for species richness. All other covariates were the same as the individual species models, and we built metro-area and individual focal city models. RESULTS We collected 5 seasons of data across 78 sites. Deployment periods for each season averaged 35 days (range: 10-51). Aside from small rodents, domestic animals, and humans, 17 mammal species were detected throughout the study period (Table 1). The final dataset included 13,689 camera trap days and 16,226 total photos. All results are reported as incidence rate ratios on the logarithmic scale (i.e. confidence intervals that cross 1 are insignificant). Mammal community metric models Mammal community diversity, as measured by Shannon’s diversity index, decreased with contaminant risk in the metro-area model ( β = -0.11; 95% CI = -0.21-0.01; p = 0.027; Fig. 2; Table 5); natural land cover and human population density had no significant impact on diversity. In the Seattle model, diversity also decreased with contaminant risk ( β = -0.20; 95% CI = -0.32, -0.07; p = 0.002; Fig. 2; Table 5), while in Tacoma, diversity was unaffected by our spatial covariates (Table 5). Species richness was not associated with any of our spatial covariates across all models (Fig. 2), but in our metro-area model, species richness was higher in Tacoma than in Seattle ( β = 1.28, CI =1.05, 1.07, p = 0.015; Table 6). Similarly to Shannon’s diversity, Pielou’s evenness was negatively associated with contaminant risk in the both-cities model ( β = -0.06, CI =-0.10, -0.01, p = 0.008; Table 7) and in the Seattle model ( β = -0.08, CI = -0.14, -0.02, p = 0.011; Table 7), but not in the Tacoma model; all other spatial covariates in the evenness models were insignificant (Fig. 2). Carnivore species activity models Models for large carnivores (black bear, mountain lion) and mustelids (river otters, weasels) did not converge due to lack of data. While the metro-area model for bobcats converged, we only had 31 total bobcat detections, and neither individual-city model converged (Table 2). For striped skunks, the metro-area and Tacoma models converged, but there was only a single skunk detection in the Seattle dataset, compared to 187 skunk detections in Tacoma (Table 1; Table 2). Hence, we did not include bobcats and striped skunks in further analyses, and moved forward with models for coyotes, raccoons, and opossums. We had 1,203 detections of coyotes (422 in the Seattle transects, 781 in the Tacoma transects), 2,503 detections of raccoons (580 in the Seattle transects, 1,923 in the Tacoma transects), and 911 detections of opossums (148 in the Seattle transects, 763 in the Tacoma transects) (Table 1). Overall, the strength and direction of relationships among spatial covariates and mesocarnivore detection rates differed across species in our metro-area model (Fig. 3). In our metro-area model, coyotes were significantly more active in areas with lower contaminant risk ( β = 0.47; 95% CI = 0.28, 0.80; p = 0.005). In contrast, raccoons and opossums had no detectable response to contaminant risk ( β = 1.42; 95% CI = 0.94, 2.14; p = 0.093 and β = 0.64; 95% CI = 0.32, 1.25; p = 0.190, respectively). Detection rates of mesocarnivores were also not affiliated with human population density or natural land cover in our metro-area model (Table 2). Because the fixed effect for city was a significant predictor for species detections ( β = 2.65; 95% CI = 1.10,6.40; p = 0.030; β = 4.11; 95% CI = 1.93, 8.72; p = 0.001; β = 6/09; 95% CI = 1.88, 19.76; p = 0.003, for coyotes, raccoons, and opossums respectively), we subsequently evaluated detections within each city’s transect. In our Seattle models (Table 3), contaminant risk remained a significant negative influence on coyote activity ( β = 0.18; 95% CI = 0.06, 0.47; p = 0.001; Fig. 3); raccoon activity increased with both contamination risk ( β = 1.91; 95% CI = 1.18, 3.09; p = 0.009) and population density ( β = 1.87; 95% CI = 1.15, 3.03; p = 0.011); and opossum activity was unaffected by any of our spatial covariates. In our Tacoma models (Table 4), human population density was negatively associated with coyote activity ( β = 0.53; 95% CI = 0.28, 0.99; p = 0.045) and positively associated with opossum activity ( β = 3.14; 95% CI = 1.15, 8.56; p = 0.025), while none of our spatial covariates predicted raccoon activity. DISCUSSION In this study, we investigated the influence of environmental contamination on mammalian diversity and activity of common mesocarnivores in our system by merging census-level data on human population density, environmental contamination risk, and land cover with camera-trap surveys. Evidence from this study suggests that environmental contamination is a salient component of habitat quality for the broader mammal community, with community diversity and community evenness showing negative relationships with contaminant risk. However, the impact of environmental contamination seems to be species-specific, with coyote activity showing pronounced negative responses to environmental contamination relative to other mesocarnivore species. Based on their avoidance of highly contaminated regions in our study area, coyotes may serve as sentinels for urban environmental quality. Contaminant risk detrimentally affected mammal community diversity and evenness, supporting our predictions. Both Shannon’s diversity index and Pielou’s evenness index decreased as contaminant risk increased in the metro area model and in the Seattle model. Conversely, species richness was not associated with any of our variables. These results suggest that while the number of species remains stable with increasing contaminant risk, the composition and evenness of the mammal community may be more sensitive to contamination. This may be for several reasons. First, habitats with high contamination risk may present additional challenges such as differences in resource availability and inhibition of dispersal through physical barriers such as large building size (Gehrt et al. 2009, Magle et al. 2016, Wang & Cao 2017). Second, habitats with high contamination risk may also exhibit direct consequences by disrupting biological processes, such as reduced reproduction (Huang et al. 2018), abnormal development (Murray et al. 2019), and depressed immune function (Eccles et al. 2021, Serieys et al. 2018). As suggested by our results among mesocarnivores, species may have different levels of sensitivity towards the cumulative impacts of contamination assessed in our study (Saaristo et al. 2018), leading to variation in how species respond to these indirect and direct pressures, and impacting community composition and diversity (Seebacher and Franklin 2012, Wingfield 2013, Aronson et al. 2017, Hassell et al. 2021). Additionally, recent work in California, wherein historical redlining policies are linked to both lower environmental quality and biodiversity, suggests a potential association between environmental quality and biodiversity, as supported by our results here (Estien et al. 2024b, 2024a). However, since wildlife communities are not equally distributed across our study area (Fig. 11), other biologically relevant factors not accounted for in our study may be influencing species richness. In our models, contaminant risk emerged as a critical predictor of coyote activity across the metro area and within our Seattle transect. While not a direct measurement of pollutants, contaminant risk is a direct reflection of landscapes experienced by wildlife as well as a proxy for a variety of environmental pollutants (Min et al. 2019, 2021). In our study area, areas with higher environmental contamination generally encompass industrial or previously industrialized zones (Abel and White 2011, Kondo et al. 2022). Along with higher pollution levels, these areas typically have less vegetation, myriad disturbances (e.g. noise and light pollution, truck activity), lower municipal services, industrial waste sites, and abandoned buildings (Silva et al. 2021, Coscia et al. 2024). Our results suggest coyotes may be more sensitive to environmentally contaminated areas. We suggest several reasons for this result: first coyotes may be impacted by non-modeled characteristics of environmentally contaminated areas. Areas with greater environmental contamination may be more difficult to navigate, as increased linear infrastructure as a result of industrialization (e.g., busy roadways, large buildings; (Wang and Cao 2017, Azhari et al. 2018), which may inhibit individuals from accessing other habitats (Kreling et al. 2024). Further, coyote activity may be driven by other factors that are linked to contamination, but are not encompassed in our models such as noise pollution (Collins et al. 2022) and lower prey diversity as reflected by our community model results. Lastly, coyotes may experience the fitness impacts of contamination more than subordinate mesocarnivores. Coyotes play a crucial role in urban ecosystems, often occupying the role of apex predator in the absence of large carnivores (Ellington and Gehrt 2019); the effects of biomagnification may impact their ability to persist in highly contaminated areas compared to raccoons and opossums and therefore reduce their activity at these sites (Rodríguez-Estival and Mateo 2019, Parker et al. 2023). Mesocarnivores have broadly been indicated as increasingly important sentinels for pollution, disease, and global change (Aguirre 2009, Marneweck et al. 2022, Parker et al. 2023, Cepeda-Duque et al. 2023, Clark-Wolf et al. 2024). While additional research is needed to understand the mechanisms by which coyotes avoid highly contaminated areas and the degree to which they are physiologically impacted by contamination, our results suggest that coyotes be considered as sentinels in studies investigating the impacts of pollution and overall environmental quality on wildlife. In contrast to coyotes, raccoon activity was positively associated with contamination risk and human population density, though only in our metro-area and Seattle models. In Tacoma only, none of our spatial covariates impacted raccoon activity. Raccoons may therefore not be as sensitive to the “negatives” of high-contaminant-risk areas, and may even be attracted to some of the attributes associated with contamination (e.g. shelter in abandoned industrial sites, subsidies from large-scale industrial food waste) (Bateman and Fleming 2012, Hansen et al. 2020). Another potential explanation is that since coyotes avoid highly contaminated areas, raccoons may select them to avoid predation by or competition with coyotes. However, while there is some evidence for temporal avoidance of coyotes by raccoons (Moura et al. 2022, Malhotra et al. 2022), research has shown that raccoons generally exhibit a lack of spatial avoidance (Gehrt and Prange 2007, Chitwood et al. 2020, Avrin et al. 2023). Instead, highly contaminated areas may act as an ecological trap for raccoons, in which individuals preferentially select for degraded habitats due to attractive features such as novel foods, despite negative physiological consequences (Battin 2004, Huang et al. 2018, Parker et al. 2023). Follow-up studies could include assessment of organismal impacts or population dynamics in raccoons in contaminated habitats to determine whether they are indeed “trapped” by these low-quality habitats. While raccoons are often positively associated with human development, as supported by our results in Seattle, this relationship is not consistent across our study area. Raccoons rely heavily on vertical territory that coyotes are not able to access (Smith and Endres 2012, Gámez and Harris 2022), and often exploit anthropogenic subsidies and waste as resources (Schulte-Hostedde et al. 2018). Therefore, perhaps the type of human development matters (Poisson et al. 2024): highly urbanized areas that are primarily residential in nature may not provide adequate refugia or food for raccoons compared to those in industrialized zones, which may present the same amount of natural land cover as dense residential areas (and therefore go unnoticed in studies that do not differentiate between the two) but offer different resources. Opossums are similarly highly synanthropic and share niches with raccoons, including the use of vertical space (Ginger et al. 2003, Voss and Jansa 2021, McTigue et al. 2024). The only significant predictor for opossum activity across our models was human population density in Tacoma, suggesting that unmodeled variables such as prey availability may be driving opossum activity. Future research is needed to understand interactions within the mesocarnivore guild in relation to environmental contamination, but we suggest that raccoons and opossums may simply be selecting for habitats where coyotes are less likely to be due to diverging resource and refugia needs. The distribution of our variables in both cities across our study sites was similar (Fig. 10; Fig. 11), but responses were different across our models. The differences in significant findings between our metro-area, Seattle, and Tacoma models suggest variability in the impact of contamination risk across different contexts and scales, and indicate that other factors may be playing a role in mediating wildlife populations and communities between Seattle and Tacoma. Our study did not consider city or neighborhood age or the relative difference in population size between the two cities. Studies across the globe suggest that neighborhood and city age impact biodiversity, but the direction of this trend is not always consistent (Aronson et al. 2014, Norton et al. 2016m Aznarez et al. 2023, Haight et al. 2023). The population size of Seattle is over three times that of Tacoma (although relative population density at our study sites were similar between the two cities; Fig. 10); however, many cameras in the Seattle transects were in suburban cities and unincorporated regions. Although Seattle and Tacoma are considered part of the same metropolitan area, sharing bus lines, commuters, and an international airport, our results suggest that these cities and their surrounding suburbs are distinct social-ecological systems. Further, our Tacoma study site supports both a richer large carnivore community and a richer overall mammal community than our Seattle study site, potentially influencing dynamics in our metro-area models (Table 1). Lastly, natural landcover and contaminant risk exhibited collinearity at our Tacoma sites, which may have impacted our results, though VIF was low (Dormann et al. 2013). Future studies using a twin- or multi-city approach should incorporate various proxies for city age, structure, and size to interrogate how varying developmental histories of urban centers within the same biome may result in divergent ecological outcomes. While the interconnectedness of human societies and urban ecosystems was not always recognized, recently, social-ecological variables have been more commonly incorporated into urban wildlife studies (Magle et al. 2016, 2021, Flockhart et al. 2016, Herrera et al. 2022, Aznarez et al. 2023, Fidino et al. 2024). Much of this literature has focused on the impact of socioeconomic metrics on wildlife, wherein neighborhoods with higher income generally have higher levels of biodiversity and environmental quality — this phenomenon is known as the ‘luxury effect’ (Hope et al. 2003, Schwarz et al. 2015, Leong et al. 2018, Chamberlain et al. 2019, Riley and Gardiner 2020). Indeed, previous analyses have found support for the luxury effect in our study area (Magle et al. 2021). Similarly, variation in urban environmental quality is often demarcated by societal inequity and structural injustices, such as political power and socioeconomic disparities (Morello-Frosch and Lopez 2006, Cushing et al. 2015, Wright 2021). However, while income can serve as a proxy for how inequities manifest in urban landscapes, the ecological factors that influence biodiversity may not always directly align with socioeconomic gradients (Kuras et al. 2020, Schell et al. 2020).These injustices and inequities have led to uneven pollution burdens that disproportionately impact marginalized human populations (Morello-Frosch and Lopez 2006, Vlahov et al. 2007, Jesdale, Bill M. et al. 2013, Yearby 2020, Mascarenhas et al. 2021, Lane et al. 2022, Swope et al. 2022, Shkembi et al. 2024)). Decades of research have shown that ethno-racial minority groups in the United States disproportionately experience effects of environmental hazards such as water pollution, noise pollution, and insufficient sanitation services, with reduced while having access to fewer environmental amenities such as tree canopy and green space (Pastor et al. 2005, Morello-Frosch and Lopez 2006, Dai 2011, Rigolon 2016, Mascarenhas et al. 2021, Estien et al. 2024b). Thus, both humans and wildlife experience differing exposure to pollution as a result of these legacies of injustice (Schell et al. 2020, Swope et al. 2022), which has consequences for disease vulnerability, behavior, and long-term mental and physical well-being (Brodin et al. 2013, Krieger et al. 2020, Levin et al. 2021, Patten et al. 2021, Lee et al. 2022). Our paper adds to an accumulating body of evidence that these disproportionately degraded environments have downstream consequences on wildlife and community ecology (Murray et al. 2022, Wood et al. 2024, Estien et al. 2024b). Here, we provide another social-ecological metric that is a more direct representation of habitat quality and may provide additional granularity for urban wildlife biologists seeking to investigate social-ecological phenomena. While this study focused on overall contamination risk and population-level analyses, future work could isolate specific pollutants that may inform the probability of wildlife presence. Data from our contamination risk variable were collected at relatively broad spatial and temporal scales, but fine-scale spatial and temporal variances in pollution are known to exist (Caubel et al. 2019). For example, a recent study in Seattle found that epiphytic moss samples from neighborhoods primarily populated by people of color had elevated heavy metals concentrations (Kondo et al. 2022). Future studies could therefore incorporate site-specific data on environmental contaminants via soil samples, air quality monitors, or other fine-scale measurements. However, to fully understand how contamination impacts urban wildlife, it will be essential to explicate the organismal and fitness consequences of pollution exposure and their projections to population-level processes. Declarations Conflict of interest statement The authors have no relevant financial or non-financial interests to disclose. Ethics statement All fieldwork was approved by local governing entities. Funding statement This research was funded by NSF grant DEB-2223973 to LRP. COE was supported by the University of California, Berkeley’s Chancellor Fellowship and the National Science Foundation Graduate Research Fellowship under DGE-419-2146752. YH was supported by the National Science Foundation Graduate Research Fellowship under DGE-214-0004 and NSF DEB-2223973. Author Contribution Y.H. led ideation, data analysis, and writing; C.O.E. contributed to ideation of the manuscript and data analysis. R.A.L., M.J.J., K.R.R., Z.H., and R.M. contributed to project administration and data curation. L.R.P. and C.R.J. led funding acquisition and contributed to ideation of the manuscript. All authors contributed to writing, reviewing and providing feedback on drafts of the manuscript. Acknowledgement We would like to acknowledge the authors of the Washington Environmental Health Disparities Map and the stakeholders, agencies, and communities that participated in its inception for providing the framework for our analyses. We thank our colleagues at the Urban Wildlife Information Network, especially Mason Fidino for facilitating data access and providing statistical advice, and Jeff Haight for providing statistical advice. The Seattle Urban Carnivore Project is coordinated by Woodland Park Zoo and Seattle University, with significant contributions of effort from many Woodland Park Zoo community science volunteers and Seattle University students. The Grit City Carnivore Project is a research partnership of Point Defiance Zoo & Aquarium, Northwest Trek Wildlife Park, and the University of Washington, and this partnership is also made possible by a dedicated team of community science volunteers and students that support the data management and field efforts of the project. Data Availability Data and analysis scripts for this study can be found at the following GitHub repository: https://github.com/yasminehentati/wa_uwin/ References Abel, T. D., and M. Stephan. 2017. Streams of toxic and hazardous waste disparities, politics and policy. Pages 311–326 in R. Holifield, J. Chakraborty, and G. Walker, editors. The Routledge Handbook of Environmental Justice. First edition. Routledge. Abel, T. D., and J. White. 2011. Skewed riskscapes and gentrified inequities: environmental exposure disparities in Seattle, Washington. American journal of public health 101 Suppl 1:S246–54. Aeluro, S., and T. J. Kavanagh. 2021. Domestic cats as environmental lead sentinels in low-income populations: a One Health pilot study sampling the fur of animals presented to a high-volume spay/neuter clinic. Environmental science and pollution research international 28:57925–57938. Aguirre, A. A. 2009. Wild canids as sentinels of ecological health: a conservation medicine perspective. Parasites & vectors 2 Suppl 1:S7. Agyeman, J., D. Schlosberg, L. Craven, and C. Matthews. 2016. Trends and Directions in Environmental Justice: From Inequity to Everyday Life, Community, and Just Sustainabilities. Annual review of environment and resources 41:321–340. Ajax, R., and J. Meyer. 1987. Policy consideration in the selection of national emission standards for hazardous air pollutants for the Tacoma smelter. Pages 709–717 Risk Assessment and Management. Springer US, Boston, MA. Aronson, M. F. J., F. A. La Sorte, C. H. Nilon, M. Katti, M. A. Goddard, C. A. Lepczyk, P. S. Warren, N. S. G. Williams, S. Cilliers, B. Clarkson, C. Dobbs, R. Dolan, M. Hedblom, S. Klotz, J. L. Kooijmans, I. Kühn, I. Macgregor-Fors, M. McDonnell, U. Mörtberg, P. Pysek, S. Siebert, J. Sushinsky, P. Werner, and M. Winter. 2014. A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers. Proceedings. Biological sciences 281:20133330. Aronson, M. F. J., C. A. Lepczyk, K. L. Evans, M. A. Goddard, S. B. Lerman, J. S. MacIvor, C. H. Nilon, and T. Vargo. 2017. Biodiversity in the city: key challenges for urban green space management. Frontiers in ecology and the environment 15:189–196. Avrin, A. C., C. E. Pekins, J. H. Sperry, and M. L. Allen. 2021. Evaluating the efficacy and decay of lures for improving carnivore detections with camera traps. Ecosphere 12. Avrin, A. C., C. E. Pekins, C. C. Wilmers, J. H. Sperry, and M. L. Allen. 2023. Can a mesocarnivore fill the functional role of an apex predator? Ecosphere 14. Azhari, A., M. T. Latif, and A. F. Mohamed. 2018. Road traffic as an air pollutant contributor within an industrial park environment. Atmospheric pollution research 9:680–687. Aznarez, C., and J. P. Pacheco. 2023. Socioeconomic and historical factors shape nature in cities. Aznarez, C., J.-C. Svenning, J. P. Pacheco, F. Have Kallesøe, F. Baró, and U. Pascual. 2023. Luxury and legacy effects on urban biodiversity, vegetation cover and ecosystem services. npj Urban Sustainability 3:1–11. Bassok, A., P. M. Hurvitz, C.-H. C. Bae, and T. Larson. 2010. Measuring neighbourhood air pollution: the case of Seattle’s international district. Journal of environmental planning and management 53:23–39. Bateman, P. W., and P. A. Fleming. 2012. Big city life: carnivores in urban environments. Journal of zoology 287:1–23. Battin, J. 2004. When good animals love bad habitats: Ecological traps and the conservation of animal populations: Ecological traps. Conservation biology: the journal of the Society for Conservation Biology 18:1482–1491. Birnie-Gauvin, K., K. S. Peiman, A. J. Gallagher, R. de Bruijn, and S. J. Cooke. 2016. Sublethal consequences of urban life for wild vertebrates. Environmental Review 24:416–425. Bramble, K., M. N. Blanco, A. Doubleday, A. J. Gassett, A. Hajat, J. D. Marshall, and L. Sheppard. 2023. Exposure disparities by income, race and ethnicity, and historic redlining grade in the greater Seattle area for ultrafine particles and other air pollutants. Environmental health perspectives 131:77004. Brand, A.-F., J. Hynes, L. A. Walker, M. Glόria Pereira, A. J. Lawlor, R. J. Williams, R. F. Shore, and E. A. Chadwick. 2020. Biological and anthropogenic predictors of metal concentration in the Eurasian otter, a sentinel of freshwater ecosystems. Environmental pollution 266:115280. Brenner, R. C., V. S. Magar, J. A. Ickes, J. E. Abbott, S. A. Stout, E. A. Crecelius, and L. S. Bingler. 2002. Characterization and FATE of PAH-Contaminated Sediments at the Wyckoff/Eagle Harbor Superfund Site. Environmental Science & Technology 36:2605–2613. Brodin, T., J. Fick, M. Jonsson, and J. Klaminder. 2013. Dilute concentrations of a psychiatric drug alter behavior of fish from natural populations. Science 339:814–815. Buckley, J. Y., M. H. Murray, N. U. de la Sancha, M. Fidino, K. A. Byers, R. Fyffe, and S. Magle. 2024. Widespread exposure to anticoagulant rodenticides among common urban mesopredators in Chicago. The Science of the total environment 952:175883. Caspi, T., J. R. Johnson, M. R. Lambert, C. J. Schell, and A. Sih. 2022. Behavioral plasticity can facilitate evolution in urban environments. Trends in ecology & evolution 37:1092–1103. Caubel, J. J., T. E. Cados, C. V. Preble, and T. W. Kirchstetter. 2019. A distributed network of 100 black carbon sensors for 100 days of air quality monitoring in West Oakland, California. Environmental science & technology 53:7564–7573. Cepeda-Duque, J. C., G. Andrade-Ponce, A. Montes-Rojas, U. Rendón-Jaramillo, V. López-Velasco, E. Arango-Correa, Á. López-Barrera, L. Mazariegos, D. D. Lizcano, A. Link, and T. G. de Oliveira. 2023. Assessing microhabitat, landscape features and intraguild relationships in the occupancy of the enigmatic and threatened Andean tiger cat (Leopardus tigrinus pardinoides) in the cloud forests of northwestern Colombia. PloS one 18:e0288247. Chamberlain, D. E., D. A. W. Henry, C. Reynolds, E. Caprio, and A. Amar. 2019. The relationship between wealth and biodiversity: A test of the Luxury Effect on bird species richness in the developing world. Global change biology 25:3045–3055. Chitwood, M. C., M. A. Lashley, S. D. Higdon, C. S. DePerno, and C. E. Moorman. 2020. Raccoon Vigilance and Activity Patterns When Sympatric with Coyotes. Diversity 12:341. Clark-Wolf, T. J., K. A. Holt, E. Johansson, A. C. Nisi, K. Rafiq, L. West, P. D. Boersma, E. L. Hazen, S. E. Moore, and B. Abrahms. 2024. The capacity of sentinel species to detect changes in environmental conditions and ecosystem structure. The journal of applied ecology 61:1638–1648. Collins, A. C., T. W. Vickers, and F. M. Shilling. 2022. Behavioral responses to anthropogenic noise at highways vary across temporal scales. Frontiers in ecology and evolution 10:891595. Collins, J. P., A. Kinzig, N. B. Grimm, W. F. Fagan, D. Hope, J. Wu, and E. T. Borer. 2000. A New Urban Ecology: Modeling human communities as integral parts of ecosystems poses special problems for the development and testing of ecological theory. American Scientist 88:416–425. Coscia, C., G. Lazzari, and I. Rubino. 2024. Industrial heritage, adaptive reuse and sustainable redevelopment scenarios: Including local communities’ multiple values in the decision-making process. Pages 347–360 Green Energy and Technology. Springer Nature Switzerland, Cham. Cushing, L., R. Morello-Frosch, M. Wander, and M. Pastor. 2015. The haves, the have-nots, and the health of everyone: the relationship between social inequality and environmental quality. Annual review of public health 36:193–209. Dai, D. 2011. Racial/ethnic and socioeconomic disparities in urban green space accessibility: Where to intervene? Landscape and urban planning 102:234–244. Des Roches, S., K. I. Brans, M. R. Lambert, L. R. Rivkin, A. M. Savage, C. J. Schell, C. Correa, L. De Meester, S. E. Diamond, N. B. Grimm, N. C. Harris, L. Govaert, A. P. Hendry, M. T. J. Johnson, J. Munshi-South, E. P. Palkovacs, M. Szulkin, M. C. Urban, B. C. Verrelli, and M. Alberti. 2021. Socio-eco-evolutionary dynamics in cities. Evolutionary applications 14:248–267. Dominoni, D. M., J. C. Borniger, and R. J. Nelson. 2016. Light at night, clocks and health: from humans to wild organisms. Biology letters 12:20160015. Dormann, C. F., J. Elith, S. Bacher, C. Buchmann, G. Carl, G. Carré, J. R. G. Marquéz, B. Gruber, B. Lafourcade, P. J. Leitão, T. Münkemüller, C. McClean, P. E. Osborne, B. Reineking, B. Schröder, A. K. Skidmore, D. Zurell, and S. Lautenbach. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27–46. Eccles, K. M., P. J. Thomas, and H. M. Chan. 2021. Spatial patterns of the exposure-response relationship between mercury and cortisol in the fur of river otter (Lontra canadensis). Chemosphere 263:127992. Ellington, E. H., and S. D. Gehrt. 2019. Behavioral responses by an apex predator to urbanization. Behavioral ecology: official journal of the International Society for Behavioral Ecology 30:821–829. Estien, C. O., M. Fidino, C. E. Wilkinson, R. Morello-Frosch, and C. J. Schell. 2024a. Historical redlining is associated with disparities in wildlife biodiversity in four California cities. Proceedings of the National Academy of Sciences of the United States of America 121:e2321441121. Estien, C. O., C. E. Wilkinson, R. Morello-Frosch, and C. J. Schell. 2024b. Historical redlining is associated with disparities in environmental quality across California. Environmental science & technology letters 11:54–59. Fatima, H., T. Mahmood, L. M. Hennelly, M. Farooq, N. Munawar, W. Ali, and B. N. Sacks. 2023. Distributional patterns and habitat association of sympatric carnivores in Margalla Hills National Park, Pakistan, and a comparison of conventional versus molecular identification in carnivore scatology. Animal biology 73:379–398. Fidino, M., G. R. Barnas, E. W. Lehrer, M. H. Murray, and S. B. Magle. 2020. Effect of Lure on Detecting Mammals with Camera Traps. Wildlife Society bulletin 44:543–552. Fidino, M., H. A. Sander, J. S. Lewis, E. W. Lehrer, K. Rivera, M. H. Murray, H. C. Adams, A. Kase, A. Flores, T. Stankowich, C. J. Schell, C. M. Salsbury, A. T. Rohnke, M. J. Jordan, A. M. Green, A. R Gramza, A. J. Zellmer, J. Williamson, T. D. Surasinghe, H. Storm, K. L. Sparks, T. J. Ryan, K. R. Remine, M. E. Pendergast, K. Mullen, D. E. Minier, C. R. Middaugh, A. L. Mertl, M. R. McClung, R. A. Long, R. N. Larson, M. T. Kohl, L. R. Harris, C. T. Hall, J. D. Haight, D. Drake, A. M. Davidge, A. O. Cheek, C. P. Bloch, E. G. Biro, W. J. B. Anthonysamy, J. L. Angstmann, M. L. Allen, S. A. Adalsteinsson, A. G. Short Gianotti, J. M. LaMontagne, T. A. Gelmi-Candusso, and S. B. Magle. 2024. Gentrification drives patterns of alpha and beta diversity in cities. Proceedings of the National Academy of Sciences of the United States of America 121:e2318596121. Flahr, L. M., N. L. Michel, A. R. D. Zahara, P. D. Jones, and C. A. Morrissey. 2015. Developmental Exposure to Aroclor 1254 Alters Migratory Behavior in Juvenile European Starlings (Sturnus vulgaris). Environmental science & technology 49:6274–6283. Flockhart, D. T. T., D. R. Norris, and J. B. Coe. 2016. Predicting free-roaming cat population densities in urban areas. Animal conservation 19:472–483. Gámez, S., and N. C. Harris. 2022. Conceptualizing the 3D niche and vertical space use. Trends in ecology & evolution 37:953–962. Gehrt, S. D., C. Anchor, and L. A. White. 2009. Home Range and Landscape Use of Coyotes in a Metropolitan Landscape: Conflict or Coexistence? Journal of mammalogy 90:1045–1057. Gehrt, S. D., and S. Prange. 2007. Interference competition between coyotes and raccoons: a test of the mesopredator release hypothesis. Behavioral ecology: official journal of the International Society for Behavioral Ecology 18:204–214. Ginger, S. M., E. C. Hellgren, M. A. Kasparian, L. P. Levesque, D. M. Engle, and D. M. Leslie Jr. 2003. Niche shift by Virginia opossum following reduction of a putative competitor, the raccoon. Journal of mammalogy 84:1279–1291. Gorjanc, G., S. Graves, and R. Heiberger. 2024. Package “car.” Graser, W. H., S. D. Gehrt, L. L. Hungerford, and C. Anchor. 2012. Variation in demographic patterns and population structure of raccoons across an urban landscape. The Journal of wildlife management 76:976–986. Grigione, M. M., P. Burman, S. Clavio, S. J. Harper, D. L. Manning, and R. J. Sarno. 2014. A comparative study between enteric parasites of Coyotes in a protected and suburban habitat. Urban Ecosystems 17:1–10. Guillette, L. J. 2000. Contaminant induced endocrine disruption in wildlife. Growth hormone & IGF research: official journal of the Growth Hormone Research Society and the International IGF Research Society 10:S45–S50. Guo, G., K. M. Eccles, M. McMillan, P. J. Thomas, H. M. Chan, and A. J. Poulain. 2020. The Gut Microbial Community Structure of the North American River Otter (Lontra canadensis) in the Alberta Oil Sands Region in Canada: Relationship with Local Environmental Variables and Metal Body Burden. Environmental toxicology and chemistry / SETAC 39:2516–2526. Guo, K., H. Liu, H. Bao, J. Hu, S. Wang, W. Zhang, Y. Zhao, and G. Jiang. 2017. Habitat selection and their interspecific interactions for mammal assemblage in the Greater Khingan Mountains, northeastern China. Wildlife biology 2017:1–8. Haight, J. D., S. J. Hall, M. Fidino, S. A. Adalsteinsson, A. A. Ahlers, J. Angstmann, W. J. B. Anthonysamy, E. Biro, M. K. Collins, B. Dugelby, T. Gallo, A. M. Green, L. Hartley, M. J. Jordan, C. A. M. Kay, E. W. Lehrer, R. A. Long, B. MacDougall, S. B. Magle, D. E. Minier, C. Mowry, M. Murray, K. Nininger, M. E. Pendergast, K. R. Remine, T. Ryan, C. Salsbury, C. J. Schell, Ç. H. Șekercioğlu, C. J. Shier, K. C. Simon, C. C. St Clair, T. Stankowich, C. J. Stevenson, L. Wayne, D. Will, J. Williamson, L. Wilson, A. J. Zellmer, and J. S. Lewis. 2023. Urbanization, climate and species traits shape mammal communities from local to continental scales. Nature ecology & evolution 7:1654–1666. Hansen, C. P., A. W. Parsons, R. Kays, and J. J. Millspaugh. 2020. Does use of backyard resources explain the abundance of urban wildlife? Frontiers in ecology and evolution 8:570771. Hartig, F., and M. F. Hartig. 2022. Package “dharma.” R Package. Available online: https://CRAN. R-project. org/package= DHARMa (accessed on 5 September 2022). Hassell, J. M., J. M. Bettridge, M. J. Ward, A. Ogendo, T. Imboma, D. Muloi, F. Fava, T. P. Robinson, M. Begon, and E. M. Fèvre. 2021. Socio-ecological drivers of vertebrate biodiversity and human-animal interfaces across an urban landscape. Global change biology 27:781–792. Hernández, F., R. E. Oldenkamp, S. Webster, J. C. Beasley, L. L. Farina, and S. M. Wisely. 2017. Raccoons (Procyon lotor) as Sentinels of Trace Element Contamination and Physiological Effects of Exposure to Coal Fly Ash. Archives of environmental contamination and toxicology 72:235–246. Herrera, D. J., M. V. Cove, W. J. McShea, S. Decker, D. T. T. Flockhart, S. M. Moore, and T. Gallo. 2022. Spatial and temporal overlap of domestic cats (Felis catus) and native urban wildlife. Frontiers in Ecology and Evolution 10. Hope, D., C. Gries, W. Zhu, W. F. Fagan, C. L. Redman, N. B. Grimm, A. L. Nelson, C. Martin, and A. Kinzig. 2003. Socioeconomics drive urban plant diversity. Proceedings of the National Academy of Sciences of the United States of America 100:8788–8792. Huang, A. C., C. Nelson, J. E. Elliott, D. A. Guertin, C. Ritland, K. Drouillard, K. M. Cheng, and H. M. Schwantje. 2018. River otters (Lontra canadensis) “trapped” in a coastal environment contaminated with persistent organic pollutants: Demographic and physiological consequences. Environmental pollution 238:306–316. Jesdale, Bill M., Morello-Frosch, Rachel, and Cushing, Lara. 2013. The Racial/Ethnic Distribution of Heat Risk–Related Land Cover in Relation to Residential Segregation. Environmental health perspectives 121:811–817. Keeler, B. L., P. Hamel, T. McPhearson, M. H. Hamann, M. L. Donahue, K. A. Meza Prado, K. K. Arkema, G. N. Bratman, K. A. Brauman, J. C. Finlay, A. D. Guerry, S. E. Hobbie, J. A. Johnson, G. K. MacDonald, R. I. McDonald, N. Neverisky, and S. A. Wood. 2019. Social-ecological and technological factors moderate the value of urban nature. Nature sustainability 2:29–38. Kok, A. C. M., B. W. Berkhout, N. V. Carlson, N. P. Evans, N. Khan, D. A. Potvin, A. N. Radford, M. Sebire, S. Shafiei Sabet, G. Shannon, and C. A. F. Wascher. 2023. How chronic anthropogenic noise can affect wildlife communities. Frontiers in ecology and evolution 11:1130075. Kondo, M. C., C. Zuidema, H. A. Moran, S. Jovan, M. Derrien, W. Brinkley, A. J. De Roos, and L. P. Tabb. 2022. Spatial predictors of heavy metal concentrations in epiphytic moss samples in Seattle, WA. The Science of the total environment 825:153801. Krieger, N., E. Wright, J. T. Chen, P. D. Waterman, E. R. Huntley, and M. Arcaya. 2020. Cancer Stage at Diagnosis, Historical Redlining, and Current Neighborhood Characteristics: Breast, Cervical, Lung, and Colorectal Cancers, Massachusetts, 2001-2015. American journal of epidemiology 189:1065–1075. Kunc, H. P., and R. Schmidt. 2019. The effects of anthropogenic noise on animals: a meta-analysis. Biology letters 15:20190649. Kuras, E. R., P. S. Warren, J. A. Zinda, M. F. J. Aronson, S. Cilliers, M. A. Goddard, C. H. Nilon, and R. Winkler. 2020. Urban socioeconomic inequality and biodiversity often converge, but not always: A global meta-analysis. Landscape and urban planning 198:103799. Lane, H. M., R. Morello-Frosch, J. D. Marshall, and J. S. Apte. 2022. Historical redlining is associated with present-day air pollution disparities in U.s. cities. Environmental science & technology letters 9:345–350. Lee, E. K., G. Donley, T. H. Ciesielski, I. Gill, O. Yamoah, A. Roche, R. Martinez, and D. A. Freedman. 2022. Health outcomes in redlined versus non-redlined neighborhoods: A systematic review and meta-analysis. Social science & medicine 294:114696. Leong, M., R. R. Dunn, and M. D. Trautwein. 2018. Biodiversity and socioeconomics in the city: a review of the luxury effect \textbar Biology Letters. Biology letters 14:20180082. Lev, E., P. H. Kahn Jr, H. Chen, and G. Esperum. 2020. Relatively wild urban parks can promote human resilience and flourishing: A case study of discovery park, Seattle, Washington. Frontiers in sustainable cities 2:497485. Levin, R., C. L. Zilli Vieira, M. H. Rosenbaum, K. Bischoff, D. C. Mordarski, and M. J. Brown. 2021. The urban lead (Pb) burden in humans, animals and the natural environment. Environmental research 193:110377. Lovell, C., S. Li, J. Turner, and C. Carbone. 2022. The effect of habitat and human disturbance on the spatiotemporal activity of two urban carnivores: The results of an intensive camera trap study. Ecology and evolution 12:e8746. Magle, S. B., M. Fidino, H. A. Sander, A. T. Rohnke, K. L. Larson, T. Gallo, C. A. M. Kay, E. W. Lehrer, M. H. Murray, S. A. Adalsteinsson, A. A. Ahlers, W. J. B. Anthonysamy, A. R. Gramza, A. M. Green, M. J. Jordan, J. S. Lewis, R. A. Long, B. MacDougall, M. E. Pendergast, K. Remine, K. C. Simon, C. C. St Clair, C. J. Shier, T. Stankowich, C. J. Stevenson, A. J. Zellmer, and C. J. Schell. 2021. Wealth and urbanization shape medium and large terrestrial mammal communities. Global change biology 27:5446–5459. Magle, S. B., E. W. Lehrer, and M. Fidino. 2016. Urban mesopredator distribution: examining the relative effects of landscape and socioeconomic factors. Animal conservation 19:163–175. Malhotra, R., S. Lima, and N. C. Harris. 2022. Temporal refuges of a subordinate carnivore vary across rural-urban gradient. Ecology and evolution 12:e9310. Mariner, P. E., F. J. Holzmer, R. E. Jackson, J. E. White, A. S. Wahl, and F. G. Wolf. 1997. Fingerprinting Arsenic Contamination in the Sediments of the Hylebos Waterway, Commencement Bay Superfund Site, Tacoma, Washington. Environmental & Engineering Geoscience III:359–368. Marneweck, C. J., B. L. Allen, A. R. Butler, E. Do Linh San, S. N. Harris, A. J. Jensen, E. A. Saldo, M. J. Somers, K. Titus, M. Muthersbaugh, A. Vanak, and D. S. Jachowski. 2022. Middle‐out ecology: small carnivores as sentinels of global change. Mammal review 52:471–479. Mascarenhas, M., R. Grattet, and K. Mege. 2021. Toxic Waste and race in twenty-first century America. Environment and society 12:108–126. McPhearson, T., S. T. A. Pickett, N. B. Grimm, J. Niemelä, M. Alberti, T. Elmqvist, C. Weber, D. Haase, J. Breuste, and S. Qureshi. 2016. Advancing urban ecology toward a science of cities. Bioscience 66:198–212. McTigue, L. E., E. V. Lassiter, M. Shaw, E. Johansson, K. Wilson, and B. A. DeGregorio. 2024. Does daily activity overlap of seven mesocarnivores vary based on human development? PloS one 19:e0288477. Min, E., D. Gruen, D. Banerjee, T. Echeverria, L. Freelander, M. Schmeltz, E. Saganić, M. Piazza, V. E. Galaviz, M. Yost, and E. Y. W. Seto. 2019. The Washington State Environmental Health Disparities Map: Development of a Community-Responsive Cumulative Impacts Assessment Tool. International journal of environmental research and public health 16:4470. Min, E., M. Piazza, V. E. Galaviz, E. Saganić, M. Schmeltz, L. Freelander, S. A. Farquhar, C. J. Karr, D. Gruen, D. Banerjee, M. Yost, and E. Y. W. Seto. 2021. Quantifying the distribution of environmental health threats and hazards in Washington State using a cumulative environmental inequality index. Environmental justice 14:298–314. Morello-Frosch, R., and R. Lopez. 2006. The riskscape and the color line: examining the role of segregation in environmental health disparities. Environmental research 102:181–196. Moura, C. W., B. Clucas, and B. J. Furnas. 2022. Humans are more influential than coyotes on mammalian mesopredator spatiotemporal activity across an urban gradient. Frontiers in ecology and evolution 10:867188. Murray, M. H., J. Buckley, K. A. Byers, K. Fake, E. W. Lehrer, S. B. Magle, C. Stone, H. Tuten, and C. J. Schell. 2022. One Health for All: Advancing Human and Ecosystem Health in Cities by Integrating an Environmental Justice Lens. Annual review of ecology, evolution, and systematics 53:403–426. Murray, M. H., J. Hill, P. Whyte, and C. C. St. Clair. 2016. Urban Compost Attracts Coyotes, Contains Toxins, and may Promote Disease in Urban-Adapted Wildlife. EcoHealth 13:285–292. Murray, M. H., C. A. Sánchez, D. J. Becker, K. A. Byers, K. E. L. Worsley‐Tonks, and M. E. Craft. 2019. City sicker? A meta-analysis of wildlife health and urbanization. Frontiers in ecology and the environment 17:575–583. Nardone, A., J. A. Casey, R. Morello-Frosch, M. Mujahid, J. R. Balmes, and N. Thakur. 2020. Associations between historical residential redlining and current age-adjusted rates of emergency department visits due to asthma across eight cities in California: an ecological study. The Lancet. Planetary health 4:e24–e31. Norton, B. A., K. L. Evans, and P. H. Warren. 2016. Urban biodiversity and landscape ecology: Patterns, processes and planning. Current landscape ecology reports 1:178–192. Oksanen, J., F. G. Blanchet, R. Kindt, P. Legendre, P. R. Minchin, R. B. O’hara, and Oksanen M. 2013. Package “vegan.” Community ecology package, version 2:1–295. Parker, K. H., J. M. Bishop, L. E. K. Serieys, R. Mateo, P. R. Camarero, and G. R. M. Leighton. 2023. A heavy burden: Metal exposure across the land-ocean continuum in an adaptable carnivore. Environmental pollution (Barking, Essex: 1987) 327:121-585. Pastor, M., R. Morello-Frosch, and J. L. Sadd. 2005. The Air Is Always Cleaner on the Other Side: Race, Space, and Ambient Air Toxics Exposures in California. Journal of urban affairs 27:127–148. Patten, K. T., A. E. Valenzuela, C. Wallis, E. L. Berg, J. L. Silverman, K. J. Bein, A. S. Wexler, and P. J. Lein. 2021. The effects of chronic exposure to ambient traffic-related air pollution on Alzheimer’s disease phenotypes in wildtype and genetically predisposed male and female rats. Environmental health perspectives 129. Petrie, A. 2020. regclass: Tools for an Introductory Class in Regression and Modeling. R package version 1:1–129. Poisson, M. K. P., F. Gebresenbet, A. R. Butler, P. Tate, D. H. Bergeron, and R. J. Moll. 2024. The way “urbanization” is defined has strong implications for its effects on mammal abundance. Urban ecosystems:1–14. Ramalho, C. E., and R. J. Hobbs. 2012. Time for a change: dynamic urban ecology. Trends in ecology & evolution 27:179–188. Richards, N. L., G. Tomy, C. A. Kinney, F. C. Nwanguma, B. Godwin, and D. A. (smith) Woollett. 2018. Using Scat Detection Dogs to Monitor Environmental Contaminants in Sentinel Species and Freshwater Ecosystems. Pages 193–262 in N. L. Richards, editor. Using Detection Dogs to Monitor Aquatic Ecosystem Health and Protect Aquatic Resources. Springer International Publishing, Cham. Rigolon, A. 2016. A complex landscape of inequity in access to urban parks: A literature review. Landscape and urban planning 153:160–169. Riley, C. B., and M. M. Gardiner. 2020. Examining the distributional equity of urban tree canopy cover and ecosystem services across United States cities. PloS one 15:e0228499. Ripley, B., B. Venables, D. M. Bates, K. Hornik, A. Gebhardt, D. Firth, and M. B. Ripley. 2013. Package “mass.” Cran r 538:113–120. Rodríguez-Estival, J., and R. Mateo. 2019. Exposure to anthropogenic chemicals in wild carnivores: a silent conservation threat demanding long-term surveillance. Current opinion in environmental science & health 11:21–25. Rodríguez-Jorquera, I. A., N. Vitale, L. Garner, D. J. Perez-Venegas, C. J. Galbán-Malagón, N. Duque-Wilckens, and G. S. Toor. 2017. Contamination of the upper class: Occurrence and effects of chemical pollutants in terrestrial top predators. Current pollution reports 3:206–219. Rosenfeld, C. S. 2017. Gut Dysbiosis in Animals Due to Environmental Chemical Exposures. Frontiers in cellular and infection microbiology 7. Saaristo, M., T. Brodin, S. Balshine, M. G. Bertram, B. W. Brooks, S. M. Ehlman, E. S. McCallum, A. Sih, J. Sundin, B. B. M. Wong, and K. E. Arnold. 2018. Direct and indirect effects of chemical contaminants on the behaviour, ecology and evolution of wildlife. Proceedings. Biological sciences / The Royal Society 285. Sanders, D., and K. J. Gaston. 2018. How ecological communities respond to artificial light at night. Journal of experimental zoology. Part A, Ecological and integrative physiology 329:394–400. Schell, C. J., K. Dyson, T. L. Fuentes, S. Des Roches, N. C. Harris, D. S. Miller, C. A. Woelfle-Erskine, and M. R. Lambert. 2020. The ecological and evolutionary consequences of systemic racism in urban environments. Science 369. Schulte-Hostedde, A. I., Z. Mazal, C. M. Jardine, and J. Gagnon. 2018. Enhanced access to anthropogenic food waste is related to hyperglycemia in raccoons (Procyon lotor). Conservation Physiology 6. Schwarz, K., M. Fragkias, C. G. Boone, W. Zhou, M. McHale, J. M. Grove, J. O’Neil-Dunne, J. P. McFadden, G. L. Buckley, D. Childers, L. Ogden, S. Pincetl, D. Pataki, A. Whitmer, and M. L. Cadenasso. 2015. Trees grow on money: urban tree canopy cover and environmental justice. PloS one 10:e0122051. Seebacher, F., and C. E. Franklin. 2012. Determining environmental causes of biological effects: the need for a mechanistic physiological dimension in conservation biology. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 367:1607–1614. Serieys, L. E. K., A. Lea, M. Epeldegui, J. Foley ,J.G. Moriarty, S.P.D. Riley, C.H. Uittenbogaart, D. Fraser, A. Mouton, and R.K. Wayne. 2018. Widespread anticoagulant poison exposure is linked with immune dysregulation and severe notoedric mange in urban bobcats. Proceedings of the 28th Vertebrate Pest Conference, 258-263. Serieys, L. E. K., A. J. Lea, M. Epeldegui, T. C. Armenta, J. Moriarty, S. VandeWoude, S. Carver, J. Foley, R. K. Wayne, S. P. D. Riley, and C. H. Uittenbogaart. 2018. Urbanization and anticoagulant poisons promote immune dysfunction in bobcats. Proceedings of the Royal Society B: Biological Sciences 285:20172533. Shakouri, A., and H. Gheytasi. 2018. Bioaccumulation of heavy metals in oyster (Saccostrea cucullata) from Chabahar bay coast in Oman Sea: Regional, seasonal and size-dependent variations. Marine pollution bulletin 126:323–329. Shkembi, A., L. M. Smith, and R. L. Neitzel. 2024. Linking environmental injustices in Detroit, MI to institutional racial segregation through historical federal redlining. Journal of exposure science & environmental epidemiology 34:389–398. Sievert, O., E. Comley, W. Phiri, and R. S. Davis. 2023. Using camera traps to assess carcass use and the intraguild dynamics of understudied African mesocarnivores. Food Webs 36:e00287. Silva, L. T., A. Magalhães, J. F. Silva, and F. Fonseca. 2021. Impacts of low-frequency noise from industrial sources in residential areas. Applied acoustics 182:108203. Smith, W. P., and K. M. Endres. 2012. Raccoon use of den trees and plant associations in western mesophytic forests: Tree attributes and availability or landscape heterogeneity? Natural resources 03:75–87. Somers, C. M. 2011. Ambient air pollution exposure and damage to male gametes: human studies and in situ “sentinel” animal experiments. Systems biology in reproductive medicine 57:63–71. Sprague, T. S. 2015. Products of Place: The Era of Reinforced-Concrete Skyscrapers in Seattle, 1921-1931. The Pacific Northwest Quarterly 106:107–119. Sugden, S., D. Sanderson, K. Ford, L. Y. Stein, and C. C. St Clair. 2020. An altered microbiome in urban coyotes mediates relationships between anthropogenic diet and poor health. Scientific reports 10:22207. Swope, C. B., D. Hernández, and L. J. Cushing. 2022. The relationship of historical redlining with present-day neighborhood environmental and health outcomes: A scoping review and conceptual model. Journal of urban health: bulletin of the New York Academy of Medicine 99:959–983. Tüzün, N., B. B. Savaşçı, and R. Stoks. 2021. Seasonal time constraints shape life history, physiology and behaviour independently, and decouple a behavioural syndrome in a damselfly. Oikos 130:274–286. Vlahov, D., N. Freudenberg, F. Proietti, D. Ompad, A. Quinn, V. Nandi, and S. Galea. 2007. Urban as a determinant of health. Journal of urban health: bulletin of the New York Academy of Medicine 84:i16–26. Vorhees, D. J., A. C. Cullen, and L. M. Altshul. 1999. Polychlorinated biphenyls in house dust and yard soil near a Superfund site. Environmental science & technology 33:2151–2156. Voss, R. S., and S. A. Jansa. 2021. Opossums: An adaptive radiation of new world marsupials. Johns Hopkins University Press, Baltimore, MD. Wainstein, M., L. B. Harding, S. M. O’Neill, D. T. Boyd, F. Koontz, B. Miller, C. F. C. Klütsch, P. J. Thomas, and G. M. Ylitalo. 2022, August 16. Highly contaminated river otters (Lontra canadensis) are effective biomonitors of environmental pollutant exposure. Springer. Wang, Y., M. L. Allen, and C. C. Wilmers. 2015. Mesopredator spatial and temporal responses to large predators and human development in the Santa Cruz Mountains of California. Biological conservation 190:23–33. Wang, Y., and Z. Cao. 2017. Industrial building environment: Old problem and new challenge. Indoor + built environment: the journal of the International Society of the Built Environment 26:1035–1039. Wingfield, J. C. 2013. Ecological processes and the ecology of stress: the impacts of abiotic environmental factors. Functional ecology 27:37–44. Wood, E. M., S. Esaian, C. Benitez, P. J. Ethington, T. Longcore, and L. Y. Pomara. 2024. Historical racial redlining and contemporary patterns of income inequality negatively affect birds, their habitat, and people in Los Angeles, California. Ornithological applications 126:1-29. Worsley-Tonks, K. E. L., E. A. Miller, S. D. Gehrt, S. C. McKenzie, D. A. Travis, T. J. Johnson, and M. E. Craft. 2020. Characterization of antimicrobial resistance genes in Enterobacteriaceae carried by suburban mesocarnivores and locally owned and stray dogs. Zoonoses and public health 67:460–466. Wright, W. J. 2021. As above, so below: Anti‐black violence as environmental racism. Antipode 53:791–809. Yamane, H., S. Tomonaga, R. Suenaga, D. M. Denbow, and M. Furuse. 2007. Intracerebroventricular injection of glutathione and its derivative induces sedative and hypnotic effects under an acute stress in neonatal chicks. Neuroscience letters 418:87–91. Yauk, C. L., G. A. Fox, B. E. McCarry, and J. S. Quinn. 2000. Induced minisatellite germline mutations in herring gulls (Larus argentatus) living near steel mills. Mutation research 452:211–218. Yearby, R. 2020. Structural Racism and Health Disparities: Reconfiguring the Social Determinants of Health Framework to Include the Root Cause. The Journal of law, medicine & ethics: a journal of the American Society of Law, Medicine & Ethics 48:518–526. Tables Tables 1 to 7 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Census tracts are filled in with a study-area-derived contamination risk scale (with 1 representing least risk and 100 representing most risk) from the Environmental Exposures and Effects indices of the Washington Environmental Health Disparities Map. Four sites (located in Eatonville, WA), indicated by the smallest red bounding box on the left side of the map, are not included in the right-hand map but are included in the analysis as part of the Tacoma transect\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/5e78566f7508f2c2681b3678.png"},{"id":80533849,"identity":"f5dfe1ed-7c08-40c5-9a2b-cfbc801e805d","added_by":"auto","created_at":"2025-04-14 11:36:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":263853,"visible":true,"origin":"","legend":"\u003cp\u003eMarginal effect plots of Shannon’s diversity (first row), species richness (second row), and Pielou’s evenness (third row) model results. Plots show contaminant risk on the x-axis and each respective community metric on the y-axis, with results depicted for (column A) the metro-area model, (column B) the Seattle-only model, and (column C) the Tacoma-only model\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/faec228992574d12277cb7c9.png"},{"id":80533534,"identity":"e043923a-1a64-4c1b-b838-367e956d8d32","added_by":"auto","created_at":"2025-04-14 11:28:29","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172700,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of coefficient estimates for species count models in the Seattle-Tacoma metropolitan area, reported as incidence rate ratios on the log scale, with a dark gray vertical line representing the intercept. Panels shown are the (A) metro-area models, (B) Seattle models, and (C) Tacoma models. Asterisks indicate significant results (p \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/e14941be5105bcba722d2ad9.jpeg"},{"id":80533531,"identity":"b3893f2d-2be0-4a48-850d-25664b1d3383","added_by":"auto","created_at":"2025-04-14 11:28:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":123133,"visible":true,"origin":"","legend":"\u003cp\u003eAll variables, both cities (with contaminant risk and environmental exposure composite variables)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/6b631e520843ccd3b47c38ec.png"},{"id":80534763,"identity":"719d78e4-742d-45bd-a356-3878f1df2f58","added_by":"auto","created_at":"2025-04-14 11:44:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":99667,"visible":true,"origin":"","legend":"\u003cp\u003eAll variables, in Seattle, as in models (with contaminant risk composite variable)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/616bafd3771e6f25981fe318.png"},{"id":80533538,"identity":"a96c8f21-77bb-41a9-b4d2-423149dab4f2","added_by":"auto","created_at":"2025-04-14 11:28:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88734,"visible":true,"origin":"","legend":"\u003cp\u003eAll variables, in Tacoma, as in models (with contaminant risk composite variable)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/d249d198a4956efc7026261b.png"},{"id":80533545,"identity":"bf8d6cc9-a14a-45f6-878f-a2e8974fbf2e","added_by":"auto","created_at":"2025-04-14 11:28:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":179098,"visible":true,"origin":"","legend":"\u003cp\u003eAll variables, both cities, with contaminant risk composite variable separated out into individual indices (with asterisks)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/f61ef5a0a790bbf778fdc3a9.png"},{"id":80533853,"identity":"bf264bb8-28c8-4e33-874e-f302ca67a91b","added_by":"auto","created_at":"2025-04-14 11:36:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":180367,"visible":true,"origin":"","legend":"\u003cp\u003eAll variables, in Seattle, with contamination risk composite variable separated out into individual indices (with asterisks)\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/12d12b7ce2bc3115210490ab.png"},{"id":80533857,"identity":"723b3bc6-7283-42ba-9534-32c3b1b58ea3","added_by":"auto","created_at":"2025-04-14 11:36:29","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":178719,"visible":true,"origin":"","legend":"\u003cp\u003eAll variables, in Tacoma with contamination risk composite variable separated out into individual indices (with asterisks)\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/6c1e91fba2f644340cf5b15e.png"},{"id":80533855,"identity":"fa138a30-173e-4840-b900-3b0e104bdbfd","added_by":"auto","created_at":"2025-04-14 11:36:29","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":289166,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of site values for environmental risk (A), population density (B), and natural land cover (C), plotted by city. Red dots represent means; black dots represent sites\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/0f7ede2c4df1d600b1f32ce3.jpeg"},{"id":80533541,"identity":"b83f7927-a38b-4fe6-ac3a-9793252167ec","added_by":"auto","created_at":"2025-04-14 11:28:29","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":151637,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of raw data for Shannon’s diversity (A), species richness (B), and Pielou’s evenness(C), plotted by city. Red dots represent means; black dots represent sites\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/db82d4163409a2740495948c.png"},{"id":85686186,"identity":"76723d8e-7b08-495b-8f6b-59e4f6c21db2","added_by":"auto","created_at":"2025-06-30 16:04:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2837124,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/0407c273-d87c-4c07-b28d-252b8095b6da.pdf"},{"id":80533851,"identity":"6b5437a0-043a-41e3-ac27-fdaf4d0d56b9","added_by":"auto","created_at":"2025-04-14 11:36:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":808931,"visible":true,"origin":"","legend":"","description":"","filename":"Table1to7.docx","url":"https://assets-eu.researchsquare.com/files/rs-6229535/v1/1fd0964212a3edf44af15576.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental contamination predicts mammal diversity and mesocarnivore activity in the Seattle- Tacoma metro area","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCities are complex landscapes where human and natural systems are intimately coupled, and are characterized by high variation in environmental quality for human and nonhuman life (Collins et al. 2000, Ramalho and Hobbs 2012, McPhearson et al. 2016, Keeler et al. 2019, Des Roches et al. 2021). Within and across urban areas, the quality of habitats and environments varies greatly across and within cities (Cushing et al. 2015, Estien et al. 2024b), influenced by factors such as green space availability; pollution sources such as industrial facilities and wastewater; and human population density. Environmental quality, or the health of an environment as defined by environmental characteristics that impact living beings, plays a large role in dictating the success of human and wildlife populations and communities in cities (Collins et al. 2000, Saaristo et al. 2018, Murray et al. 2019, Lev et al. 2020, Fatima et al. 2023). In addition to factors such as competition, mating opportunities, and availability of food and shelter, urban wildlife must attenuate their responses directly to environmental quality (e.g. heterogeneity in noise pollution, contaminated water sources, green space size and complexity, etc.). For example, increased anxiety in birds in response to pollution exposure can suppress distress vocalizations (Yamane et al. 2007).\u003c/p\u003e \u003cp\u003eEnvironmental quality in cities is heavily impacted by various types of pollution (i.e. environmental contamination, (Agyeman et al. 2016), which can result in deleterious health effects or even lethal consequences in both wildlife and humans (Dominoni et al. 2016, Birnie-Gauvin et al. 2016, Sanders and Gaston 2018, Kunc and Schmidt 2019, Kok et al. 2023). Contaminant exposure in free-ranging wildlife is associated with parasitic disease outbreaks (Murray et al. 2016, Serieys et al. 2018), behavioral differences (Brodin et al. 2013, Flahr et al. 2015, T\u0026uuml;z\u0026uuml;n et al. 2021), gut dysbiosis (Rosenfeld 2017), reduced fertility (Somers 2011), and endocrine disruption (Guillette 2000). Extensive \u003cem\u003ein situ\u003c/em\u003e experimental research exists on the impacts of pollution in mammals, wherein animals are placed in \u0026ldquo;exposure facilities\u0026rdquo; and exposed to environmental contamination, acting as sentinels for the impacts of contamination (Somers 2011). For example, one study drew air from a tunnel into a laboratory vivarium to expose study animals to air pollution from a freeway tunnel (Patten et al. 2021). Work using this methodology has uncovered evidence of genetic mutations (Yauk et al. 2000), altered behavior (Saaristo et al. 2018), and physiological imbalances (Guo et al. 2020). Recently, studies on wild mammals as sentinels of environmental contamination wherein free-ranging mammals live in contaminated environments have allowed researchers to assess population-wide effects (Wainstein et al. 2022). Individual-level impacts on physiology and behavior may further scale up to affect community-level processes, such as competition and predation, by altering the presence and distribution of populations (Saaristo et al. 2018). However, it is difficult to determine population- and community-level effects in an experimental setting. Therefore, knowledge on the consequences of contamination on urban-adapted mammals, and subsequent impact on population and community dynamics, remains limited. While spatial patterns of environmental quality vary widely within and among cities, prior work investigating the social-ecological factors contributing to those patterns provide leverage to examine how gradients of quality impact urban wildlife populations (Cushing et al. 2015, Rigolon 2016, Lane et al. 2022, Estien et al. 2024b).\u003c/p\u003e \u003cp\u003eWithin urban mammal communities, carnivores are uniquely vulnerable to the health impacts of pollutants due to biomagnification, the exacerbation of contaminant exposure in higher trophic levels (Rodr\u0026iacute;guez-Jorquera et al. 2017). However, due to their large home range requirements, lower population densities, and sensitivity to human activity, large carnivores such as mountain lions (\u003cem\u003ePuma concolor\u003c/em\u003e) and gray wolves (\u003cem\u003eCanis lupus\u003c/em\u003e) are less common within urban regions. Conversely, mesocarnivores have been highly successful in urban areas due to dietary and behavioral flexibilities that facilitate persistence in novel or disturbed environmental contexts (Bateman and Fleming 2012, Caspi et al. 2022). However, these traits can also expose urban mesocarnivores to unique environmental risks (Murray et al. 2016, Richards et al. 2018, Serieys et al. 2018, Shakouri and Gheytasi 2018). The impact of environmental contamination may be more apparent in synanthropic mesocarnivores such as coyotes (\u003cem\u003eCanis latrans\u003c/em\u003e) and raccoons (\u003cem\u003eProcyon lotor\u003c/em\u003e) due to their better ability to exploit and persist in urban environments compared to larger carnivores, which may avoid highly contaminated areas entirely due to their overlap with human activity (Marneweck et al. 2022). Due to their success across urbanization gradients, mesocarnivores may act as sentinels for environmental contamination caused by industrial activities (Hern\u0026aacute;ndez et al. 2017, Brand et al. 2020, Guo et al. 2020, Aeluro and Kavanagh 2021). For example, the Green-Duwamish River, a waterway in Washington state, USA, begins in the Cascades mountain range but passes through a highly industrialized region dominated by historic Superfund sites before finally emptying into the Puget Sound in the industrial district of the city of Seattle. Along this tributary, river otters living closer to Superfund sites had increased polychlorinated biphenyls (PCB) concentrations in their scat (Wainstein et al. 2022).\u003c/p\u003e \u003cp\u003eIn Seattle and its neighboring city Tacoma, dense human populations exist in conjunction with large-scale shipping and industrial activity. Both Seattle and Tacoma are economically significant port cities and are throughways of major interstates. Further, the region (known as the Seattle-Tacoma metropolitan area) hosts an abundance of environmental contaminant sources, such as National Priorities List sites (a.k.a. Superfund sites; sites defined by the U.S. Environmental Protection Agency as containing hazardous substances that pose risks to human health and the environment due to industrial pollution, improper waste disposal, or chemical spills [Mariner et al. 1997, Abel and Stephan 2017]), historic smelting operations, (Ajax and Meyer 1987), steel plants (Abel and White 2011, Sprague 2015), hazardous waste sites (e.g. asphalt facilities; (Abel and Stephan 2017), and airplane plants (Abel and White 2011). As in many U.S. cities and indeed, across the globe, the burden of pollution in the Seattle-Tacoma region often falls on communities of color and working-class communities (Bassok et al. 2010, Kondo et al. 2022, Bramble et al. 2023). Pollutants linked to these contaminant sources include PCBs (Vorhees et al. 1999), polycyclic aromatic hydrocarbons (PAH) (Brenner et al. 2002), heavy metals (Mariner et al. 1997), and particulate matter (Abel and White 2011). The legacy of such industrial and waste-producing sites is also long-standing; even in cases where they are removed and rebuilt into residential areas, contaminants still persist in the environment (Abel and Stephan 2017, Bramble et al. 2023). Consequently, wildlife communities in this region are likely experiencing variable contaminated landscapes, with potential consequences for biodiversity. Still, the region hosts a vibrant community of wildlife common to western North America. The Seattle-Tacoma metropolitan area is therefore a fitting case study for exploring the impacts of environmental contaminants on wildlife.\u003c/p\u003e \u003cp\u003eThe primary objective of this study was to determine the impacts of environmental contamination on wildlife activity in the Seattle-Tacoma metropolitan region, Washington, USA, at two taxonomic scales: first, the medium-to-large mammal community, and second, for specific carnivore species. Our secondary objective was to determine the relative impact of environmental contamination risk, as defined by a previous study on environmental contamination across Washington census tracts (Min et al. 2021, relative to commonly-analyzed spatial covariates known to be affiliated with urban wildlife activity: proportion of natural land cover and human population density. We predicted that areas with higher proportion of natural land cover and lower environmental contamination risk would have greater relative mammalian diversity, richness, and evenness compared to sites with less natural land cover and those with higher environmental contamination risk. We also predicted higher detection rates of carnivore species in areas with higher proportion of natural land cover and lower contaminant risk. Within this pattern, we expected mesocarnivores to respond more strongly to contaminant risk; conversely, we expected larger carnivores to respond more strongly to proportion of natural land cover and human population density.\u003c/p\u003e \u003cp\u003eTo address our objectives, we analyzed camera trap data with generalized linear mixed models (GLMMs) to determine whether three spatial covariates \u0026ndash; environmental contamination risk, human population density, and proportion of natural land cover \u0026ndash; were associated with a) mammalian community metrics (diversity, richness, and evenness) and b) detection rates (i.e., activity) of carnivores. Our mammal community included 17 species ranging from rodents to ungulates to carnivores (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We also analyzed these relationships for 9 individual carnivore species in our study area (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), but due to data limitations, our results focus on 3 common mesocarnivore species in our study area: raccoons, coyotes, and Virginia opossums (\u003cem\u003eDidelphis virginiana\u003c/em\u003e). Raccoons are the quintessential synanthrope, able to exploit anthropogenic resources amidst urban habitat fragmentation (Graser et al. 2012). Coyotes are equally successful urban exploiters that have witnessed rapid geographic expansion, establishing populations in a cadre of biomes and ecosystems throughout the North American continent (Grigione et al. 2014, Murray et al. 2016, Sugden et al. 2020). Lastly, while they are taxonomically marsupials, Virginia opossums (hereafter opossums) occupy similar trophic positions to sympatric mesocarnivores, and are persistent across urbanization gradients in North American cities (Wang et al. 2015, Worsley-Tonks et al. 2020, Buckley et al. 2024).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStudy area and design\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCameras were deployed as part of the Seattle Urban Carnivore Project (Seattle, Washington), and the Grit City Carnivore Project (Tacoma, Washington) (total \u003cem\u003en\u003c/em\u003e = 33 cameras and n = 41 cameras, respectively; Fig. 1). These cameras were deployed along urban-exurban gradients following protocols from the Urban Wildlife Information Network (UWIN) camera study design protocol, with cameras placed in parks along multiple transects at least 1 km apart along the designated gradient (Haight et al. 2023, Fidino et al. 2024). Camera transects spanned the cities of Seattle and Tacoma as well as surrounding cities and towns in King and Pierce Counties, including Bothell, Redmond, Tukwila, Renton, Auburn, Puyallup, and Eatonville (Fig. 1). \u003c/p\u003e\n\u003cp\u003eDuring each season (spring, summer, fall, winter), cameras were deployed for a minimum of 28 consecutive days, barring theft, vandalism, or camera malfunction. Generally, cameras were deployed in April, July, October, and January, and removed from the field after a four-week total deployment period. Cameras were deployed for a total of 5 seasons (January 2019-January 2020). The UWIN study design protocol during this time period included deployment of fatty acid scent discs paired with each camera trap, which have been found to have no impact on mammal detection rates (Fidino et al. 2020). Images were identified to the species level by trained staff, students, and volunteers.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSpatial covariates \u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll spatial analyses were done in the R computing software. For our natural land cover covariate, we calculated proportions for each land cover type using data from the National Land Cover Database. We then combined all natural land cover types to create a final proportion of natural land cover for each 30m raster pixel. In our study area, these cover types include various forest, shrubland, herbaceous, wetland, and cultivated land classes (https://mrlc.gov; Haight et al. 2023). For human population density, we used openly available data from the University of Wisconsin (https://silvis.forest.wisc.edu/data/housing-block-change/); these layers were calculated at the census block level using U.S. census data from 2020. Data from census blocks in our study area were then rasterized using the R packages \u003cem\u003esf \u003c/em\u003eand \u003cem\u003eterra\u003c/em\u003e. Values of both natural land cover and human population density were averaged around each camera site in a 1km buffer using the R package \u003cem\u003eterra \u003c/em\u003e(Gehrt et al. 2009, Magle et al. 2016). \u003c/p\u003e\n\u003cp\u003eTo quantify environmental contamination risk, we used data from The Washington Environmental Health Disparities Map, which developed publicly available indices estimating cumulative impacts of environmental pollution burden experienced in Washington communities (Min et al. 2019, 2021); https://fortress.wa.gov/doh/wtnibl/WTNIBL/). The Washington Health Disparities Map and similar datasets (e.g. CalEnviroScreen in California) are widely used in urban planning, human health, and ecological studies (Cushing et al. 2015, Nardone et al. 2020, Min et al. 2021, Estien et al. 2024b). In this map, pollution burden is divided into environmental exposures, which are direct measurements of pollutants (i.e. toxic releases from industrial facilities, particulate matter 2.5 (PM\u003csub\u003e2.5\u003c/sub\u003e) concentrations, ozone concentrations, and diesel exhaust emissions); and environmental effects (i.e. lead risk from housing, proximity to hazardous waste treatment storage and disposal facilities, proximity to national priorities list facilities [Superfund sites], and proximity to risk management plan facilities), which represent adverse environmental qualities that are not direct exposures, but still pose potential health risks. We created two composite variables: \u0026ldquo;environmental exposures\u0026rdquo; and \u0026ldquo;contamination risk\u0026rdquo; (i.e. environmental effects, which we renamed for clarity). To create these variables, we used methods from Min et al. (2021), which quantified projected pollution burden in human communities. In brief, values of each index were downloaded from the Washington Environmental Health Disparities Map website and converted to spatial data in R using census tract GEOIDs. Each individual index was calculated at a 1 km buffer around each camera trap site using the R packages \u003cem\u003eterra\u003c/em\u003e. Indices were then averaged within their group to create the final composite \u0026ldquo;environmental exposure\u0026rdquo; and \u0026ldquo;contamination risk\u0026rdquo; composite variables for the entire dataset. We excluded one variable from Min et al. from our contamination risk composite variable \u0026ndash; wastewater discharge from treatment plants \u0026ndash; because data was missing for over half of our census tracts. We then scaled each composite variable based on values at all study sites, such that a score of 0 represents no burden, and a score of 1 represents the highest burden. This scaling was done separately for the metro-area dataset and each city-specific dataset. Each variable therefore represents the average exposure or risk experienced around the site in relation to other sites in the dataset to be analyzed. We found that, at our study sites, environmental exposure and contamination risk were relatively strongly correlated (R = 0.68, Fig. 4). We therefore chose to proceed with only contamination risk, because it approximates proximity to landscape features (i.e. industrial facilities causing contamination) that have a direct influence on habitat quality. Prior to running models, we assessed variance inflation factors for these and all final variables using the \u003cem\u003eregclass \u003c/em\u003epackage in R, which suggested low multicollinearity (VIF \u0026lt; 2 [Petrie 2020]). \u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe used generalized linear mixed models (GLMMs) to evaluate the effects of our spatial covariates on mammal community diversity and carnivore species activity. Analyses were restricted to sites with 3 or more active seasons and at least 18 active camera-trap nights per season (Magle et al. 2016). Photos within 30 minutes of each other were removed to ensure temporal independence between detections (Guo et al. 2017). For the mammal community diversity model, we removed all non-mammals as well as rodents smaller than squirrels from the dataset, as consistent capture of small mammals and other wildlife taxa (e.g. birds, herpetofauna) with camera traps is unreliable. We also removed domestic dogs, cats, and humans. \u003c/p\u003e\n\u003cp\u003eUsing our GLMM framework, we modeled both mammal community diversity and detection rates of carnivores within and across the two focal cities. We modeled the two cities together (\u0026ldquo;metro-area model\u0026rdquo;) as well as separately to investigate how our selected spatial covariates were affiliated with our response variables both across the metro area and within each focal city transect. We used detection rates as a proxy for activity at each site (often referred to as relative abundance) (Lovell et al. 2022, Sievert et al. 2023). We used the glmmTMB package in R to fit our models (Avrin et al. 2021). We included season as a variable due to its potential role in variation of wildlife activity throughout the year (e.g. dispersal, mating seasons). To account for non-independence among seasons sampled at the same station, site was included as a random effect in all models. We scaled all continuous variables with a mean value of 0 and standard deviation of 1 within the model. GLMMs were built for each species of interest, with species detections per site per season as the response variable (Lovell et al. 2022, Sievert et al. 2023). We determined that the negative binomial distribution was the best-fitting discrete distribution for each species using the \u003cem\u003ecar\u003c/em\u003e and \u003cem\u003eMASS\u003c/em\u003e packages in R (Ripley et al. 2013, Gorjanc et al. 2024). Focal city, contamination risk, natural land cover, human population density, and season were included as fixed effects in the \u0026ldquo;metro-area\u0026rdquo; model. In individual city models, all variables except focal city were included. We used an offset on the log scale to account for variation in the number of sampling occasions at each site (i.e., the number of days the camera was operational during a given season). Model diagnostics were performed using the \u003cem\u003edharma\u003c/em\u003e package in R (Hartig and Hartig 2022).\u003c/p\u003e\n\u003cp\u003eTo model mammal community diversity, we calculated Shannon\u0026rsquo;s diversity index, species richness, and Pielou\u0026rsquo;s evenness for the mammal community at each site/season combination using the \u003cem\u003evegan\u003c/em\u003e package in R (Oksanen et al. 2013). We then averaged these values across seasons. A GLMM was built and fit to a Gaussian distribution for Shannon\u0026rsquo;s diversity and Pielou\u0026rsquo;s evenness, and a Poisson distribution for species richness. All other covariates were the same as the individual species models, and we built metro-area and individual focal city models.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eWe collected 5 seasons of data across 78 sites. Deployment periods for each season averaged 35 days (range: 10-51). Aside from small rodents, domestic animals, and humans, 17 mammal species were detected throughout the study period (Table 1). The final dataset included 13,689 camera trap days and 16,226 total photos. All results are reported as incidence rate ratios on the logarithmic scale (i.e. confidence intervals that cross 1 are insignificant).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMammal community metric models\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMammal community diversity, as measured by Shannon\u0026rsquo;s diversity index, decreased with contaminant risk in the metro-area model (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e-0.11; 95% CI = -0.21-0.01; p = 0.027; Fig. 2; Table 5); natural land cover and human population density had no significant impact on diversity. In the Seattle model, diversity also decreased with contaminant risk (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e-0.20; 95% CI = -0.32, -0.07; p = 0.002; Fig. 2; Table 5), while in Tacoma, diversity was unaffected by our spatial covariates (Table 5). Species richness was not associated with any of our spatial covariates across all models (Fig. 2), but in our metro-area model, species richness was higher in Tacoma than in Seattle (\u003cem\u003e\u0026beta;\u003c/em\u003e = 1.28, CI =1.05, 1.07, p = 0.015; Table 6). Similarly to Shannon\u0026rsquo;s diversity, Pielou\u0026rsquo;s evenness was negatively associated with contaminant risk in the both-cities model (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.06, CI =-0.10, -0.01, p = 0.008; Table 7) and in the Seattle model (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.08, CI = -0.14, -0.02, p = 0.011; Table 7), but not in the Tacoma model; all other spatial covariates in the evenness models were insignificant (Fig. 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCarnivore species activity models \u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eModels for large carnivores (black bear, mountain lion) and mustelids (river otters, weasels) did not converge due to lack of data. While the metro-area model for bobcats converged, we only had 31 total bobcat detections, and neither individual-city model converged (Table 2). For striped skunks, the metro-area and Tacoma models converged, but there was only a single skunk detection in the Seattle dataset, compared to 187 skunk detections in Tacoma (Table 1; Table 2). Hence, we did not include bobcats and striped skunks in further analyses, and moved forward with models for coyotes, raccoons, and opossums. We had 1,203 detections of coyotes (422 in the Seattle transects, 781 in the Tacoma transects), 2,503 detections of raccoons (580 in the Seattle transects, 1,923 in the Tacoma transects), and 911 detections of opossums (148 in the Seattle transects, 763 in the Tacoma transects) (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, the strength and direction of relationships among spatial covariates and mesocarnivore detection rates differed across species in our metro-area model (Fig. 3). In our metro-area model, coyotes were significantly more active in areas with lower contaminant risk (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e0.47; 95% CI = 0.28, 0.80; p = 0.005). In contrast, raccoons and opossums had no detectable response to contaminant risk (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e1.42; 95% CI = 0.94, 2.14; p = 0.093 and \u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e0.64; 95% CI = 0.32, 1.25; p = 0.190, respectively). Detection rates of mesocarnivores were also not affiliated with human population density or natural land cover in our metro-area model (Table 2). Because the fixed effect for city was a significant predictor for species detections (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e2.65; 95% CI = 1.10,6.40; p = 0.030; \u0026nbsp;\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e4.11; 95% CI = 1.93, 8.72; p = 0.001; \u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e6/09; 95% CI = 1.88, 19.76; p = 0.003, for coyotes, raccoons, and opossums respectively), we subsequently evaluated detections within each city\u0026rsquo;s transect. In our Seattle models (Table 3), contaminant risk remained a significant negative influence on coyote activity (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e0.18; 95% CI = 0.06, 0.47; p = 0.001; Fig. 3); raccoon activity increased with both contamination risk (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e1.91; 95% CI = 1.18, 3.09; p = 0.009) and population density (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e1.87; 95% CI = 1.15, 3.03; p = 0.011); and opossum activity was unaffected by any of our spatial covariates. In our Tacoma models (Table 4), human population density was negatively associated with coyote activity (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e0.53; 95% CI = 0.28, 0.99; p = 0.045) and positively associated with opossum activity (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e3.14; 95% CI = 1.15, 8.56; p = 0.025), while none of our spatial covariates predicted raccoon activity.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we investigated the influence of environmental contamination on mammalian diversity and activity of common mesocarnivores in our system by merging census-level data on human population density, environmental contamination risk, and land cover with camera-trap surveys. Evidence from this study suggests that environmental contamination is a salient component of habitat quality for the broader mammal community, with community diversity and community evenness showing negative relationships with contaminant risk. However, the impact of environmental contamination seems to be species-specific, with coyote activity showing pronounced negative responses to environmental contamination relative to other mesocarnivore species. Based on their avoidance of highly contaminated regions in our study area, coyotes may serve as sentinels for urban environmental quality. \u003c/p\u003e\n\n\u003cp\u003eContaminant risk detrimentally affected mammal community diversity and evenness, supporting our predictions. Both Shannon\u0026rsquo;s diversity index and Pielou\u0026rsquo;s evenness index decreased as contaminant risk increased in the metro area model and in the Seattle model. Conversely, species richness was not associated with any of our variables. These results suggest that while the number of species remains stable with increasing contaminant risk, the composition and evenness of the mammal community may be more sensitive to contamination. This may be for several reasons. First, habitats with high contamination risk may present additional challenges such as differences in resource availability and inhibition of dispersal through physical barriers such as large building size (Gehrt et al. 2009, Magle et al. 2016, Wang \u0026amp; Cao 2017). Second, habitats with high contamination risk may also exhibit direct consequences by disrupting biological processes, such as reduced reproduction (Huang et al. 2018), abnormal development (Murray et al. 2019), and depressed immune function (Eccles et al. 2021, Serieys et al. 2018). As suggested by our results among mesocarnivores, species may have different levels of sensitivity towards the cumulative impacts of contamination assessed in our study (Saaristo et al. 2018), leading to variation in how species respond to these indirect and direct pressures, and impacting community composition and diversity (Seebacher and Franklin 2012, Wingfield 2013, Aronson et al. 2017, Hassell et al. 2021). Additionally, recent work in California, wherein historical redlining policies are linked to both lower environmental quality and biodiversity, suggests a potential association between environmental quality and biodiversity, as supported by our results here (Estien et al. 2024b, 2024a). However, since wildlife communities are not equally distributed across our study area (Fig. 11), other biologically relevant factors not accounted for in our study may be influencing species richness. \u003c/p\u003e\n\n\u003cp\u003eIn our models, contaminant risk emerged as a critical predictor of coyote activity across the metro area and within our Seattle transect. While not a direct measurement of pollutants, contaminant risk is a direct reflection of landscapes experienced by wildlife as well as a proxy for a variety of environmental pollutants (Min et al. 2019, 2021). In our study area, areas with higher environmental contamination generally encompass industrial or previously industrialized zones (Abel and White 2011, Kondo et al. 2022). Along with higher pollution levels, these areas typically have less vegetation, myriad disturbances (e.g. noise and light pollution, truck activity), lower municipal services, industrial waste sites, and abandoned buildings (Silva et al. 2021, Coscia et al. 2024). Our results suggest coyotes may be more sensitive to environmentally contaminated areas. We suggest several reasons for this result: first coyotes may be impacted by non-modeled characteristics of environmentally contaminated areas. Areas with greater environmental contamination may be more difficult to navigate, as increased linear infrastructure as a result of industrialization (e.g., busy roadways, large buildings; (Wang and Cao 2017, Azhari et al. 2018), which may inhibit individuals from accessing other habitats (Kreling et al. 2024). Further, coyote activity may be driven by other factors that are linked to contamination, but are not encompassed in our models such as noise pollution (Collins et al. 2022) and lower prey diversity as reflected by our community model results. Lastly, coyotes may experience the fitness impacts of contamination more than subordinate mesocarnivores. Coyotes play a crucial role in urban ecosystems, often occupying the role of apex predator in the absence of large carnivores (Ellington and Gehrt 2019); the effects of biomagnification may impact their ability to persist in highly contaminated areas compared to raccoons and opossums and therefore reduce their activity at these sites (Rodr\u0026iacute;guez-Estival and Mateo 2019, Parker et al. 2023). Mesocarnivores have broadly been indicated as increasingly important sentinels for pollution, disease, and global change (Aguirre 2009, Marneweck et al. 2022, Parker et al. 2023, Cepeda-Duque et al. 2023, Clark-Wolf et al. 2024). While additional research is needed to understand the mechanisms by which coyotes avoid highly contaminated areas and the degree to which they are physiologically impacted by contamination, our results suggest that coyotes be considered as sentinels in studies investigating the impacts of pollution and overall environmental quality on wildlife. \u003c/p\u003e\n\n\u003cp\u003eIn contrast to coyotes, raccoon activity was positively associated with contamination risk and human population density, though only in our metro-area and Seattle models. In Tacoma only, none of our spatial covariates impacted raccoon activity. Raccoons may therefore not be as sensitive to the \u0026ldquo;negatives\u0026rdquo; of high-contaminant-risk areas, and may even be attracted to some of the attributes associated with contamination (e.g. shelter in abandoned industrial sites, subsidies from large-scale industrial food waste) (Bateman and Fleming 2012, Hansen et al. 2020). Another potential explanation is that since coyotes avoid highly contaminated areas, raccoons may select them to avoid predation by or competition with coyotes. However, while there is some evidence for temporal avoidance of coyotes by raccoons (Moura et al. 2022, Malhotra et al. 2022), research has shown that raccoons generally exhibit a lack of spatial avoidance (Gehrt and Prange 2007, Chitwood et al. 2020, Avrin et al. 2023). Instead, highly contaminated areas may act as an ecological trap for raccoons, in which individuals preferentially select for degraded habitats due to attractive features such as novel foods, despite negative physiological consequences (Battin 2004, Huang et al. 2018, Parker et al. 2023). Follow-up studies could include assessment of organismal impacts or population dynamics in raccoons in contaminated habitats to determine whether they are indeed \u0026ldquo;trapped\u0026rdquo; by these low-quality habitats. While raccoons are often positively associated with human development, as supported by our results in Seattle, this relationship is not consistent across our study area. Raccoons rely heavily on vertical territory that coyotes are not able to access (Smith and Endres 2012, G\u0026aacute;mez and Harris 2022), and often exploit anthropogenic subsidies and waste as resources (Schulte-Hostedde et al. 2018). Therefore, perhaps the type of human development matters (Poisson et al. 2024): highly urbanized areas that are primarily residential in nature may not provide adequate refugia or food for raccoons compared to those in industrialized zones, which may present the same amount of natural land cover as dense residential areas (and therefore go unnoticed in studies that do not differentiate between the two) but offer different resources. Opossums are similarly highly synanthropic and share niches with raccoons, including the use of vertical space (Ginger et al. 2003, Voss and Jansa 2021, McTigue et al. 2024). The only significant predictor for opossum activity across our models was human population density in Tacoma, suggesting that unmodeled variables such as prey availability may be driving opossum activity. Future research is needed to understand interactions within the mesocarnivore guild in relation to environmental contamination, but we suggest that raccoons and opossums may simply be selecting for habitats where coyotes are less likely to be due to diverging resource and refugia needs.\u003c/p\u003e\n\n\u003cp\u003eThe distribution of our variables in both cities across our study sites was similar (Fig. 10; Fig. 11), but responses were different across our models. The differences in significant findings between our metro-area, Seattle, and Tacoma models suggest variability in the impact of contamination risk across different contexts and scales, and indicate that other factors may be playing a role in mediating wildlife populations and communities between Seattle and Tacoma. Our study did not consider city or neighborhood age or the relative difference in population size between the two cities. Studies across the globe suggest that neighborhood and city age impact biodiversity, but the direction of this trend is not always consistent (Aronson et al. 2014, Norton et al. 2016m Aznarez et al. 2023, Haight et al. 2023). The population size of Seattle is over three times that of Tacoma (although relative population density at our study sites were similar between the two cities; Fig. 10); however, many cameras in the Seattle transects were in suburban cities and unincorporated regions. Although Seattle and Tacoma are considered part of the same metropolitan area, sharing bus lines, commuters, and an international airport, our results suggest that these cities and their surrounding suburbs are distinct social-ecological systems. Further, our Tacoma study site supports both a richer large carnivore community and a richer overall mammal community than our Seattle study site, potentially influencing dynamics in our metro-area models (Table 1). Lastly, natural landcover and contaminant risk exhibited collinearity at our Tacoma sites, which may have impacted our results, though VIF was low (Dormann et al. 2013). Future studies using a twin- or multi-city approach should incorporate various proxies for city age, structure, and size to interrogate how varying developmental histories of urban centers within the same biome may result in divergent ecological outcomes. \u003c/p\u003e\n\n\u003cp\u003eWhile the interconnectedness of human societies and urban ecosystems was not always recognized, recently, social-ecological variables have been more commonly incorporated into urban wildlife studies (Magle et al. 2016, 2021, Flockhart et al. 2016, Herrera et al. 2022, Aznarez et al. 2023, Fidino et al. 2024). Much of this literature has focused on the impact of socioeconomic metrics on wildlife, wherein neighborhoods with higher income generally have higher levels of biodiversity and environmental quality \u0026mdash; this phenomenon is known as the \u0026lsquo;luxury effect\u0026rsquo; (Hope et al. 2003, Schwarz et al. 2015, Leong et al. 2018, Chamberlain et al. 2019, Riley and Gardiner 2020). Indeed, previous analyses have found support for the luxury effect in our study area (Magle et al. 2021). Similarly, variation in urban environmental quality is often demarcated by societal inequity and structural injustices, such as political power and socioeconomic disparities (Morello-Frosch and Lopez 2006, Cushing et al. 2015, Wright 2021). However, while income can serve as a proxy for how inequities manifest in urban landscapes, the ecological factors that influence biodiversity may not always directly align with socioeconomic gradients (Kuras et al. 2020, Schell et al. 2020).These injustices and inequities have led to uneven pollution burdens that disproportionately impact marginalized human populations (Morello-Frosch and Lopez 2006, Vlahov et al. 2007, Jesdale, Bill M. et al. 2013, Yearby 2020, Mascarenhas et al. 2021, Lane et al. 2022, Swope et al. 2022, Shkembi et al. 2024)). Decades of research have shown that ethno-racial minority groups in the United States disproportionately experience effects of environmental hazards such as water pollution, noise pollution, and insufficient sanitation services, with reduced while having access to fewer environmental amenities such as tree canopy and green space (Pastor et al. 2005, Morello-Frosch and Lopez 2006, Dai 2011, Rigolon 2016, Mascarenhas et al. 2021, Estien et al. 2024b). Thus, both humans and wildlife experience differing exposure to pollution as a result of these legacies of injustice (Schell et al. 2020, Swope et al. 2022), which has consequences for disease vulnerability, behavior, and long-term mental and physical well-being (Brodin et al. 2013, Krieger et al. 2020, Levin et al. 2021, Patten et al. 2021, Lee et al. 2022). Our paper adds to an accumulating body of evidence that these disproportionately degraded environments have downstream consequences on wildlife and community ecology (Murray et al. 2022, Wood et al. 2024, Estien et al. 2024b). \u003c/p\u003e\n\n\u003cp\u003eHere, we provide another social-ecological metric that is a more direct representation of habitat quality and may provide additional granularity for urban wildlife biologists seeking to investigate social-ecological phenomena. While this study focused on overall contamination risk and population-level analyses, future work could isolate specific pollutants that may inform the probability of wildlife presence. Data from our contamination risk variable were collected at relatively broad spatial and temporal scales, but fine-scale spatial and temporal variances in pollution are known to exist (Caubel et al. 2019). For example, a recent study in Seattle found that epiphytic moss samples from neighborhoods primarily populated by people of color had elevated heavy metals concentrations (Kondo et al. 2022). Future studies could therefore incorporate site-specific data on environmental contaminants via soil samples, air quality monitors, or other fine-scale measurements. However, to fully understand how contamination impacts urban wildlife, it will be essential to explicate the organismal and fitness consequences of pollution exposure and their projections to population-level processes. \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest statement\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eEthics statement\u003c/h2\u003e\n\u003cp\u003eAll fieldwork was approved by local governing entities.\u003c/p\u003e\n\u003ch2\u003eFunding statement\u003c/h2\u003e\n\u003cp\u003eThis research was funded by NSF grant DEB-2223973 to LRP. COE was supported by the University of California, Berkeley\u0026rsquo;s Chancellor Fellowship and the National Science Foundation Graduate Research Fellowship under DGE-419-2146752. YH was supported by the National Science Foundation Graduate Research Fellowship under DGE-214-0004 and NSF DEB-2223973.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eY.H. led ideation, data analysis, and writing; C.O.E. contributed to ideation of the manuscript and data analysis. R.A.L., M.J.J., K.R.R., Z.H., and R.M. contributed to project administration and data curation. L.R.P. and C.R.J. led funding acquisition and contributed to ideation of the manuscript. All authors contributed to writing, reviewing and providing feedback on drafts of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe would like to acknowledge the authors of the Washington Environmental Health Disparities Map and the stakeholders, agencies, and communities that participated in its inception for providing the framework for our analyses. We thank our colleagues at the Urban Wildlife Information Network, especially Mason Fidino for facilitating data access and providing statistical advice, and Jeff Haight for providing statistical advice. The Seattle Urban Carnivore Project is coordinated by Woodland Park Zoo and Seattle University, with significant contributions of effort from many Woodland Park Zoo community science volunteers and Seattle University students. The Grit City Carnivore Project is a research partnership of Point Defiance Zoo \u0026amp; Aquarium, Northwest Trek Wildlife Park, and the University of Washington, and this partnership is also made possible by a dedicated team of community science volunteers and students that support the data management and field efforts of the project.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData and analysis scripts for this study can be found at the following GitHub repository: https://github.com/yasminehentati/wa_uwin/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbel, T. D., and M. Stephan. 2017. Streams of toxic and hazardous waste disparities, politics and policy. Pages 311\u0026ndash;326 \u003cem\u003ein\u003c/em\u003e R. Holifield, J. Chakraborty, and G. Walker, editors. The Routledge Handbook of Environmental Justice. First edition. Routledge.\u003c/li\u003e\n\u003cli\u003eAbel, T. D., and J. White. 2011. Skewed riskscapes and gentrified inequities: environmental exposure disparities in Seattle, Washington. American journal of public health 101 Suppl 1:S246\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eAeluro, S., and T. J. Kavanagh. 2021. Domestic cats as environmental lead sentinels in low-income populations: a One Health pilot study sampling the fur of animals presented to a high-volume spay/neuter clinic. Environmental science and pollution research international 28:57925\u0026ndash;57938.\u003c/li\u003e\n\u003cli\u003eAguirre, A. A. 2009. Wild canids as sentinels of ecological health: a conservation medicine perspective. Parasites \u0026amp; vectors 2 Suppl 1:S7.\u003c/li\u003e\n\u003cli\u003eAgyeman, J., D. Schlosberg, L. Craven, and C. Matthews. 2016. Trends and Directions in Environmental Justice: From Inequity to Everyday Life, Community, and Just Sustainabilities. Annual review of environment and resources 41:321\u0026ndash;340.\u003c/li\u003e\n\u003cli\u003eAjax, R., and J. Meyer. 1987. Policy consideration in the selection of national emission standards for hazardous air pollutants for the Tacoma smelter. Pages 709\u0026ndash;717 Risk Assessment and Management. Springer US, Boston, MA.\u003c/li\u003e\n\u003cli\u003eAronson, M. F. J., F. A. La Sorte, C. H. Nilon, M. Katti, M. A. Goddard, C. A. Lepczyk, P. S. Warren, N. S. G. Williams, S. Cilliers, B. Clarkson, C. Dobbs, R. Dolan, M. Hedblom, S. Klotz, J. L. Kooijmans, I. K\u0026uuml;hn, I. Macgregor-Fors, M. McDonnell, U. M\u0026ouml;rtberg, P. Pysek, S. Siebert, J. Sushinsky, P. Werner, and M. Winter. 2014. A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers. Proceedings. Biological sciences 281:20133330.\u003c/li\u003e\n\u003cli\u003eAronson, M. F. J., C. A. Lepczyk, K. L. Evans, M. A. Goddard, S. B. Lerman, J. S. MacIvor, C. H. Nilon, and T. Vargo. 2017. Biodiversity in the city: key challenges for urban green space management. Frontiers in ecology and the environment 15:189\u0026ndash;196.\u003c/li\u003e\n\u003cli\u003eAvrin, A. C., C. E. Pekins, J. H. Sperry, and M. L. Allen. 2021. Evaluating the efficacy and decay of lures for improving carnivore detections with camera traps. Ecosphere 12.\u003c/li\u003e\n\u003cli\u003eAvrin, A. C., C. E. Pekins, C. C. Wilmers, J. H. Sperry, and M. L. Allen. 2023. Can a mesocarnivore fill the functional role of an apex predator? Ecosphere 14.\u003c/li\u003e\n\u003cli\u003eAzhari, A., M. T. Latif, and A. F. Mohamed. 2018. Road traffic as an air pollutant contributor within an industrial park environment. Atmospheric pollution research 9:680\u0026ndash;687.\u003c/li\u003e\n\u003cli\u003eAznarez, C., and J. P. Pacheco. 2023. Socioeconomic and historical factors shape nature in cities.\u003c/li\u003e\n\u003cli\u003eAznarez, C., J.-C. Svenning, J. P. Pacheco, F. Have Kalles\u0026oslash;e, F. Bar\u0026oacute;, and U. Pascual. 2023. Luxury and legacy effects on urban biodiversity, vegetation cover and ecosystem services. npj Urban Sustainability 3:1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eBassok, A., P. M. Hurvitz, C.-H. C. Bae, and T. Larson. 2010. Measuring neighbourhood air pollution: the case of Seattle\u0026rsquo;s international district. Journal of environmental planning and management 53:23\u0026ndash;39.\u003c/li\u003e\n\u003cli\u003eBateman, P. W., and P. A. Fleming. 2012. Big city life: carnivores in urban environments. Journal of zoology 287:1\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eBattin, J. 2004. When good animals love bad habitats: Ecological traps and the conservation of animal populations: Ecological traps. Conservation biology: the journal of the Society for Conservation Biology 18:1482\u0026ndash;1491.\u003c/li\u003e\n\u003cli\u003eBirnie-Gauvin, K., K. S. Peiman, A. J. Gallagher, R. de Bruijn, and S. J. Cooke. 2016. Sublethal consequences of urban life for wild vertebrates. Environmental Review 24:416\u0026ndash;425.\u003c/li\u003e\n\u003cli\u003eBramble, K., M. N. Blanco, A. Doubleday, A. J. Gassett, A. Hajat, J. D. Marshall, and L. Sheppard. 2023. Exposure disparities by income, race and ethnicity, and historic redlining grade in the greater Seattle area for ultrafine particles and other air pollutants. Environmental health perspectives 131:77004.\u003c/li\u003e\n\u003cli\u003eBrand, A.-F., J. Hynes, L. A. Walker, M. Glόria Pereira, A. J. Lawlor, R. J. Williams, R. F. Shore, and E. A. Chadwick. 2020. Biological and anthropogenic predictors of metal concentration in the Eurasian otter, a sentinel of freshwater ecosystems. Environmental pollution 266:115280.\u003c/li\u003e\n\u003cli\u003eBrenner, R. C., V. S. Magar, J. A. Ickes, J. E. Abbott, S. A. Stout, E. A. Crecelius, and L. S. Bingler. 2002. Characterization and FATE of PAH-Contaminated Sediments at the Wyckoff/Eagle Harbor Superfund Site. Environmental Science \u0026amp; Technology 36:2605\u0026ndash;2613.\u003c/li\u003e\n\u003cli\u003eBrodin, T., J. Fick, M. Jonsson, and J. Klaminder. 2013. Dilute concentrations of a psychiatric drug alter behavior of fish from natural populations. Science 339:814\u0026ndash;815.\u003c/li\u003e\n\u003cli\u003eBuckley, J. Y., M. H. Murray, N. U. de la Sancha, M. Fidino, K. A. Byers, R. Fyffe, and S. Magle. 2024. Widespread exposure to anticoagulant rodenticides among common urban mesopredators in Chicago. The Science of the total environment 952:175883.\u003c/li\u003e\n\u003cli\u003eCaspi, T., J. R. Johnson, M. R. Lambert, C. J. Schell, and A. Sih. 2022. Behavioral plasticity can facilitate evolution in urban environments. Trends in ecology \u0026amp; evolution 37:1092\u0026ndash;1103.\u003c/li\u003e\n\u003cli\u003eCaubel, J. J., T. E. Cados, C. V. Preble, and T. W. Kirchstetter. 2019. A distributed network of 100 black carbon sensors for 100 days of air quality monitoring in West Oakland, California. Environmental science \u0026amp; technology 53:7564\u0026ndash;7573.\u003c/li\u003e\n\u003cli\u003eCepeda-Duque, J. C., G. Andrade-Ponce, A. Montes-Rojas, U. Rend\u0026oacute;n-Jaramillo, V. L\u0026oacute;pez-Velasco, E. Arango-Correa, \u0026Aacute;. L\u0026oacute;pez-Barrera, L. Mazariegos, D. D. Lizcano, A. Link, and T. G. de Oliveira. 2023. Assessing microhabitat, landscape features and intraguild relationships in the occupancy of the enigmatic and threatened Andean tiger cat (Leopardus tigrinus pardinoides) in the cloud forests of northwestern Colombia. PloS one 18:e0288247.\u003c/li\u003e\n\u003cli\u003eChamberlain, D. E., D. A. W. Henry, C. Reynolds, E. Caprio, and A. Amar. 2019. The relationship between wealth and biodiversity: A test of the Luxury Effect on bird species richness in the developing world. Global change biology 25:3045\u0026ndash;3055.\u003c/li\u003e\n\u003cli\u003eChitwood, M. C., M. A. Lashley, S. D. Higdon, C. S. DePerno, and C. E. Moorman. 2020. Raccoon Vigilance and Activity Patterns When Sympatric with Coyotes. Diversity 12:341.\u003c/li\u003e\n\u003cli\u003eClark-Wolf, T. J., K. A. Holt, E. Johansson, A. C. Nisi, K. Rafiq, L. West, P. D. Boersma, E. L. Hazen, S. E. Moore, and B. Abrahms. 2024. The capacity of sentinel species to detect changes in environmental conditions and ecosystem structure. The journal of applied ecology 61:1638\u0026ndash;1648.\u003c/li\u003e\n\u003cli\u003eCollins, A. C., T. W. Vickers, and F. M. Shilling. 2022. Behavioral responses to anthropogenic noise at highways vary across temporal scales. Frontiers in ecology and evolution 10:891595.\u003c/li\u003e\n\u003cli\u003eCollins, J. P., A. Kinzig, N. B. Grimm, W. F. Fagan, D. Hope, J. Wu, and E. T. Borer. 2000. A New Urban Ecology: Modeling human communities as integral parts of ecosystems poses special problems for the development and testing of ecological theory. American Scientist 88:416\u0026ndash;425.\u003c/li\u003e\n\u003cli\u003eCoscia, C., G. Lazzari, and I. Rubino. 2024. Industrial heritage, adaptive reuse and sustainable redevelopment scenarios: Including local communities\u0026rsquo; multiple values in the decision-making process. Pages 347\u0026ndash;360 Green Energy and Technology. Springer Nature Switzerland, Cham.\u003c/li\u003e\n\u003cli\u003eCushing, L., R. Morello-Frosch, M. Wander, and M. Pastor. 2015. The haves, the have-nots, and the health of everyone: the relationship between social inequality and environmental quality. Annual review of public health 36:193\u0026ndash;209.\u003c/li\u003e\n\u003cli\u003eDai, D. 2011. Racial/ethnic and socioeconomic disparities in urban green space accessibility: Where to intervene? Landscape and urban planning 102:234\u0026ndash;244.\u003c/li\u003e\n\u003cli\u003eDes Roches, S., K. I. Brans, M. R. Lambert, L. R. Rivkin, A. M. Savage, C. J. Schell, C. Correa, L. De Meester, S. E. Diamond, N. B. Grimm, N. C. Harris, L. Govaert, A. P. Hendry, M. T. J. Johnson, J. Munshi-South, E. P. Palkovacs, M. Szulkin, M. C. Urban, B. C. Verrelli, and M. Alberti. 2021. Socio-eco-evolutionary dynamics in cities. Evolutionary applications 14:248\u0026ndash;267.\u003c/li\u003e\n\u003cli\u003eDominoni, D. M., J. C. Borniger, and R. J. Nelson. 2016. Light at night, clocks and health: from humans to wild organisms. Biology letters 12:20160015.\u003c/li\u003e\n\u003cli\u003eDormann, C. F., J. Elith, S. Bacher, C. Buchmann, G. Carl, G. Carr\u0026eacute;, J. R. G. Marqu\u0026eacute;z, B. Gruber, B. Lafourcade, P. J. Leit\u0026atilde;o, T. M\u0026uuml;nkem\u0026uuml;ller, C. McClean, P. E. Osborne, B. Reineking, B. Schr\u0026ouml;der, A. K. Skidmore, D. Zurell, and S. Lautenbach. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27\u0026ndash;46.\u003c/li\u003e\n\u003cli\u003eEccles, K. M., P. J. Thomas, and H. M. Chan. 2021. Spatial patterns of the exposure-response relationship between mercury and cortisol in the fur of river otter (Lontra canadensis). Chemosphere 263:127992.\u003c/li\u003e\n\u003cli\u003eEllington, E. H., and S. D. Gehrt. 2019. Behavioral responses by an apex predator to urbanization. Behavioral ecology: official journal of the International Society for Behavioral Ecology 30:821\u0026ndash;829.\u003c/li\u003e\n\u003cli\u003eEstien, C. O., M. Fidino, C. E. Wilkinson, R. Morello-Frosch, and C. J. Schell. 2024a. Historical redlining is associated with disparities in wildlife biodiversity in four California cities. Proceedings of the National Academy of Sciences of the United States of America 121:e2321441121.\u003c/li\u003e\n\u003cli\u003eEstien, C. O., C. E. Wilkinson, R. Morello-Frosch, and C. J. Schell. 2024b. Historical redlining is associated with disparities in environmental quality across California. Environmental science \u0026amp; technology letters 11:54\u0026ndash;59.\u003c/li\u003e\n\u003cli\u003eFatima, H., T. Mahmood, L. M. Hennelly, M. Farooq, N. Munawar, W. Ali, and B. N. Sacks. 2023. Distributional patterns and habitat association of sympatric carnivores in Margalla Hills National Park, Pakistan, and a comparison of conventional versus molecular identification in carnivore scatology. Animal biology 73:379\u0026ndash;398.\u003c/li\u003e\n\u003cli\u003eFidino, M., G. R. Barnas, E. W. Lehrer, M. H. Murray, and S. B. Magle. 2020. Effect of Lure on Detecting Mammals with Camera Traps. Wildlife Society bulletin 44:543\u0026ndash;552.\u003c/li\u003e\n\u003cli\u003eFidino, M., H. A. Sander, J. S. Lewis, E. W. Lehrer, K. Rivera, M. H. Murray, H. C. Adams, A. Kase, A. Flores, T. Stankowich, C. J. Schell, C. M. Salsbury, A. T. Rohnke, M. J. Jordan, A. M. Green, A. R Gramza, A. J. Zellmer, J. Williamson, T. D. Surasinghe, H. Storm, K. L. Sparks, T. J. Ryan, K. R. Remine, M. E. Pendergast, K. Mullen, D. E. Minier, C. R. Middaugh, A. L. Mertl, M. R. McClung, R. A. Long, R. N. Larson, M. T. Kohl, L. R. Harris, C. T. Hall, J. D. Haight, D. Drake, A. M. Davidge, A. O. Cheek, C. P. Bloch, E. G. Biro, W. J. B. Anthonysamy, J. L. Angstmann, M. L. Allen, S. A. Adalsteinsson, A. G. Short Gianotti, J. M. LaMontagne, T. A. Gelmi-Candusso, and S. B. Magle. 2024. Gentrification drives patterns of alpha and beta diversity in cities. Proceedings of the National Academy of Sciences of the United States of America 121:e2318596121.\u003c/li\u003e\n\u003cli\u003eFlahr, L. M., N. L. Michel, A. R. D. Zahara, P. D. Jones, and C. A. Morrissey. 2015. Developmental Exposure to Aroclor 1254 Alters Migratory Behavior in Juvenile European Starlings (Sturnus vulgaris). Environmental science \u0026amp; technology 49:6274\u0026ndash;6283.\u003c/li\u003e\n\u003cli\u003eFlockhart, D. T. T., D. R. Norris, and J. B. Coe. 2016. Predicting free-roaming cat population densities in urban areas. Animal conservation 19:472\u0026ndash;483.\u003c/li\u003e\n\u003cli\u003eG\u0026aacute;mez, S., and N. C. Harris. 2022. Conceptualizing the 3D niche and vertical space use. Trends in ecology \u0026amp; evolution 37:953\u0026ndash;962.\u003c/li\u003e\n\u003cli\u003eGehrt, S. D., C. Anchor, and L. A. White. 2009. Home Range and Landscape Use of Coyotes in a Metropolitan Landscape: Conflict or Coexistence? Journal of mammalogy 90:1045\u0026ndash;1057.\u003c/li\u003e\n\u003cli\u003eGehrt, S. D., and S. Prange. 2007. Interference competition between coyotes and raccoons: a test of the mesopredator release hypothesis. Behavioral ecology: official journal of the International Society for Behavioral Ecology 18:204\u0026ndash;214.\u003c/li\u003e\n\u003cli\u003eGinger, S. M., E. C. Hellgren, M. A. Kasparian, L. P. Levesque, D. M. Engle, and D. M. Leslie Jr. 2003. Niche shift by Virginia opossum following reduction of a putative competitor, the raccoon. Journal of mammalogy 84:1279\u0026ndash;1291.\u003c/li\u003e\n\u003cli\u003eGorjanc, G., S. Graves, and R. Heiberger. 2024. Package \u0026ldquo;car.\u0026rdquo;\u003c/li\u003e\n\u003cli\u003eGraser, W. H., S. D. Gehrt, L. L. Hungerford, and C. Anchor. 2012. Variation in demographic patterns and population structure of raccoons across an urban landscape. The Journal of wildlife management 76:976\u0026ndash;986.\u003c/li\u003e\n\u003cli\u003eGrigione, M. M., P. Burman, S. Clavio, S. J. Harper, D. L. Manning, and R. J. Sarno. 2014. A comparative study between enteric parasites of Coyotes in a protected and suburban habitat. Urban Ecosystems 17:1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eGuillette, L. J. 2000. Contaminant induced endocrine disruption in wildlife. Growth hormone \u0026amp; IGF research: official journal of the Growth Hormone Research Society and the International IGF Research Society 10:S45\u0026ndash;S50.\u003c/li\u003e\n\u003cli\u003eGuo, G., K. M. Eccles, M. McMillan, P. J. Thomas, H. M. Chan, and A. J. Poulain. 2020. The Gut Microbial Community Structure of the North American River Otter (Lontra canadensis) in the Alberta Oil Sands Region in Canada: Relationship with Local Environmental Variables and Metal Body Burden. Environmental toxicology and chemistry / SETAC 39:2516\u0026ndash;2526.\u003c/li\u003e\n\u003cli\u003eGuo, K., H. Liu, H. Bao, J. Hu, S. Wang, W. Zhang, Y. Zhao, and G. Jiang. 2017. Habitat selection and their interspecific interactions for mammal assemblage in the Greater Khingan Mountains, northeastern China. Wildlife biology 2017:1\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eHaight, J. D., S. J. Hall, M. Fidino, S. A. Adalsteinsson, A. A. Ahlers, J. Angstmann, W. J. B. Anthonysamy, E. Biro, M. K. Collins, B. Dugelby, T. Gallo, A. M. Green, L. Hartley, M. J. Jordan, C. A. M. Kay, E. W. Lehrer, R. A. Long, B. MacDougall, S. B. Magle, D. E. Minier, C. Mowry, M. Murray, K. Nininger, M. E. Pendergast, K. R. Remine, T. Ryan, C. Salsbury, C. J. Schell, \u0026Ccedil;. H. Șekercioğlu, C. J. Shier, K. C. Simon, C. C. St Clair, T. Stankowich, C. J. Stevenson, L. Wayne, D. Will, J. Williamson, L. Wilson, A. J. Zellmer, and J. S. Lewis. 2023. Urbanization, climate and species traits shape mammal communities from local to continental scales. Nature ecology \u0026amp; evolution 7:1654\u0026ndash;1666.\u003c/li\u003e\n\u003cli\u003eHansen, C. P., A. W. Parsons, R. Kays, and J. J. Millspaugh. 2020. Does use of backyard resources explain the abundance of urban wildlife? Frontiers in ecology and evolution 8:570771.\u003c/li\u003e\n\u003cli\u003eHartig, F., and M. F. Hartig. 2022. Package \u0026ldquo;dharma.\u0026rdquo; R Package. Available online: https://CRAN. R-project. org/package= DHARMa (accessed on 5 September 2022).\u003c/li\u003e\n\u003cli\u003eHassell, J. M., J. M. Bettridge, M. J. Ward, A. Ogendo, T. Imboma, D. Muloi, F. Fava, T. P. Robinson, M. Begon, and E. M. F\u0026egrave;vre. 2021. Socio-ecological drivers of vertebrate biodiversity and human-animal interfaces across an urban landscape. Global change biology 27:781\u0026ndash;792.\u003c/li\u003e\n\u003cli\u003eHern\u0026aacute;ndez, F., R. E. Oldenkamp, S. Webster, J. C. Beasley, L. L. Farina, and S. M. Wisely. 2017. Raccoons (Procyon lotor) as Sentinels of Trace Element Contamination and Physiological Effects of Exposure to Coal Fly Ash. Archives of environmental contamination and toxicology 72:235\u0026ndash;246.\u003c/li\u003e\n\u003cli\u003eHerrera, D. J., M. V. Cove, W. J. McShea, S. Decker, D. T. T. Flockhart, S. M. Moore, and T. Gallo. 2022. Spatial and temporal overlap of domestic cats (Felis catus) and native urban wildlife. Frontiers in Ecology and Evolution 10.\u003c/li\u003e\n\u003cli\u003eHope, D., C. Gries, W. Zhu, W. F. Fagan, C. L. Redman, N. B. Grimm, A. L. Nelson, C. Martin, and A. Kinzig. 2003. Socioeconomics drive urban plant diversity. Proceedings of the National Academy of Sciences of the United States of America 100:8788\u0026ndash;8792.\u003c/li\u003e\n\u003cli\u003eHuang, A. C., C. Nelson, J. E. Elliott, D. A. Guertin, C. Ritland, K. Drouillard, K. M. Cheng, and H. M. Schwantje. 2018. River otters (Lontra canadensis) \u0026ldquo;trapped\u0026rdquo; in a coastal environment contaminated with persistent organic pollutants: Demographic and physiological consequences. Environmental pollution 238:306\u0026ndash;316.\u003c/li\u003e\n\u003cli\u003eJesdale, Bill M., Morello-Frosch, Rachel, and Cushing, Lara. 2013. The Racial/Ethnic Distribution of Heat Risk\u0026ndash;Related Land Cover in Relation to Residential Segregation. Environmental health perspectives 121:811\u0026ndash;817.\u003c/li\u003e\n\u003cli\u003eKeeler, B. L., P. Hamel, T. McPhearson, M. H. Hamann, M. L. Donahue, K. A. Meza Prado, K. K. Arkema, G. N. Bratman, K. A. Brauman, J. C. Finlay, A. D. Guerry, S. E. Hobbie, J. A. Johnson, G. K. MacDonald, R. I. McDonald, N. Neverisky, and S. A. Wood. 2019. Social-ecological and technological factors moderate the value of urban nature. Nature sustainability 2:29\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003eKok, A. C. M., B. W. Berkhout, N. V. Carlson, N. P. Evans, N. Khan, D. A. Potvin, A. N. Radford, M. Sebire, S. Shafiei Sabet, G. Shannon, and C. A. F. Wascher. 2023. How chronic anthropogenic noise can affect wildlife communities. Frontiers in ecology and evolution 11:1130075.\u003c/li\u003e\n\u003cli\u003eKondo, M. C., C. Zuidema, H. A. Moran, S. Jovan, M. Derrien, W. Brinkley, A. J. De Roos, and L. P. Tabb. 2022. Spatial predictors of heavy metal concentrations in epiphytic moss samples in Seattle, WA. The Science of the total environment 825:153801.\u003c/li\u003e\n\u003cli\u003eKrieger, N., E. Wright, J. T. Chen, P. D. Waterman, E. R. Huntley, and M. Arcaya. 2020. Cancer Stage at Diagnosis, Historical Redlining, and Current Neighborhood Characteristics: Breast, Cervical, Lung, and Colorectal Cancers, Massachusetts, 2001-2015. American journal of epidemiology 189:1065\u0026ndash;1075.\u003c/li\u003e\n\u003cli\u003eKunc, H. P., and R. Schmidt. 2019. The effects of anthropogenic noise on animals: a meta-analysis. Biology letters 15:20190649.\u003c/li\u003e\n\u003cli\u003eKuras, E. R., P. S. Warren, J. A. Zinda, M. F. J. Aronson, S. Cilliers, M. A. Goddard, C. H. Nilon, and R. Winkler. 2020. Urban socioeconomic inequality and biodiversity often converge, but not always: A global meta-analysis. Landscape and urban planning 198:103799.\u003c/li\u003e\n\u003cli\u003eLane, H. M., R. Morello-Frosch, J. D. Marshall, and J. S. Apte. 2022. Historical redlining is associated with present-day air pollution disparities in U.s. cities. Environmental science \u0026amp; technology letters 9:345\u0026ndash;350.\u003c/li\u003e\n\u003cli\u003eLee, E. K., G. Donley, T. H. Ciesielski, I. Gill, O. Yamoah, A. Roche, R. Martinez, and D. A. Freedman. 2022. Health outcomes in redlined versus non-redlined neighborhoods: A systematic review and meta-analysis. Social science \u0026amp; medicine 294:114696.\u003c/li\u003e\n\u003cli\u003eLeong, M., R. R. Dunn, and M. D. Trautwein. 2018. Biodiversity and socioeconomics in the city: a review of the luxury effect \\textbar Biology Letters. Biology letters 14:20180082.\u003c/li\u003e\n\u003cli\u003eLev, E., P. H. Kahn Jr, H. Chen, and G. Esperum. 2020. Relatively wild urban parks can promote human resilience and flourishing: A case study of discovery park, Seattle, Washington. Frontiers in sustainable cities 2:497485.\u003c/li\u003e\n\u003cli\u003eLevin, R., C. L. Zilli Vieira, M. H. Rosenbaum, K. Bischoff, D. C. Mordarski, and M. J. Brown. 2021. The urban lead (Pb) burden in humans, animals and the natural environment. Environmental research 193:110377.\u003c/li\u003e\n\u003cli\u003eLovell, C., S. Li, J. Turner, and C. Carbone. 2022. The effect of habitat and human disturbance on the spatiotemporal activity of two urban carnivores: The results of an intensive camera trap study. Ecology and evolution 12:e8746.\u003c/li\u003e\n\u003cli\u003eMagle, S. B., M. Fidino, H. A. Sander, A. T. Rohnke, K. L. Larson, T. Gallo, C. A. M. Kay, E. W. Lehrer, M. H. Murray, S. A. Adalsteinsson, A. A. Ahlers, W. J. B. Anthonysamy, A. R. Gramza, A. M. Green, M. J. Jordan, J. S. Lewis, R. A. Long, B. MacDougall, M. E. Pendergast, K. Remine, K. C. Simon, C. C. St Clair, C. J. Shier, T. Stankowich, C. J. Stevenson, A. J. Zellmer, and C. J. Schell. 2021. Wealth and urbanization shape medium and large terrestrial mammal communities. Global change biology 27:5446\u0026ndash;5459.\u003c/li\u003e\n\u003cli\u003eMagle, S. B., E. W. Lehrer, and M. Fidino. 2016. Urban mesopredator distribution: examining the relative effects of landscape and socioeconomic factors. Animal conservation 19:163\u0026ndash;175.\u003c/li\u003e\n\u003cli\u003eMalhotra, R., S. Lima, and N. C. Harris. 2022. Temporal refuges of a subordinate carnivore vary across rural-urban gradient. Ecology and evolution 12:e9310.\u003c/li\u003e\n\u003cli\u003eMariner, P. E., F. J. Holzmer, R. E. Jackson, J. E. White, A. S. Wahl, and F. G. Wolf. 1997. Fingerprinting Arsenic Contamination in the Sediments of the Hylebos Waterway, Commencement Bay Superfund Site, Tacoma, Washington. Environmental \u0026amp; Engineering Geoscience III:359\u0026ndash;368.\u003c/li\u003e\n\u003cli\u003eMarneweck, C. J., B. L. Allen, A. R. Butler, E. Do Linh San, S. N. Harris, A. J. Jensen, E. A. Saldo, M. J. Somers, K. Titus, M. Muthersbaugh, A. Vanak, and D. S. Jachowski. 2022. Middle‐out ecology: small carnivores as sentinels of global change. Mammal review 52:471\u0026ndash;479.\u003c/li\u003e\n\u003cli\u003eMascarenhas, M., R. Grattet, and K. Mege. 2021. Toxic Waste and race in twenty-first century America. Environment and society 12:108\u0026ndash;126.\u003c/li\u003e\n\u003cli\u003eMcPhearson, T., S. T. A. Pickett, N. B. Grimm, J. Niemel\u0026auml;, M. Alberti, T. Elmqvist, C. Weber, D. Haase, J. Breuste, and S. Qureshi. 2016. Advancing urban ecology toward a science of cities. Bioscience 66:198\u0026ndash;212.\u003c/li\u003e\n\u003cli\u003eMcTigue, L. E., E. V. Lassiter, M. Shaw, E. Johansson, K. Wilson, and B. A. DeGregorio. 2024. Does daily activity overlap of seven mesocarnivores vary based on human development? PloS one 19:e0288477.\u003c/li\u003e\n\u003cli\u003eMin, E., D. Gruen, D. Banerjee, T. Echeverria, L. Freelander, M. Schmeltz, E. Saganić, M. Piazza, V. E. Galaviz, M. Yost, and E. Y. W. Seto. 2019. The Washington State Environmental Health Disparities Map: Development of a Community-Responsive Cumulative Impacts Assessment Tool. International journal of environmental research and public health 16:4470.\u003c/li\u003e\n\u003cli\u003eMin, E., M. Piazza, V. E. Galaviz, E. Saganić, M. Schmeltz, L. Freelander, S. A. Farquhar, C. J. Karr, D. Gruen, D. Banerjee, M. Yost, and E. Y. W. Seto. 2021. Quantifying the distribution of environmental health threats and hazards in Washington State using a cumulative environmental inequality index. Environmental justice 14:298\u0026ndash;314.\u003c/li\u003e\n\u003cli\u003eMorello-Frosch, R., and R. Lopez. 2006. The riskscape and the color line: examining the role of segregation in environmental health disparities. Environmental research 102:181\u0026ndash;196.\u003c/li\u003e\n\u003cli\u003eMoura, C. W., B. Clucas, and B. J. Furnas. 2022. Humans are more influential than coyotes on mammalian mesopredator spatiotemporal activity across an urban gradient. Frontiers in ecology and evolution 10:867188.\u003c/li\u003e\n\u003cli\u003eMurray, M. H., J. Buckley, K. A. Byers, K. Fake, E. W. Lehrer, S. B. Magle, C. Stone, H. Tuten, and C. J. Schell. 2022. One Health for All: Advancing Human and Ecosystem Health in Cities by Integrating an Environmental Justice Lens. Annual review of ecology, evolution, and systematics 53:403\u0026ndash;426.\u003c/li\u003e\n\u003cli\u003eMurray, M. H., J. Hill, P. Whyte, and C. C. St. Clair. 2016. Urban Compost Attracts Coyotes, Contains Toxins, and may Promote Disease in Urban-Adapted Wildlife. EcoHealth 13:285\u0026ndash;292.\u003c/li\u003e\n\u003cli\u003eMurray, M. H., C. A. S\u0026aacute;nchez, D. J. Becker, K. A. Byers, K. E. L. Worsley‐Tonks, and M. E. Craft. 2019. City sicker? A meta-analysis of wildlife health and urbanization. Frontiers in ecology and the environment 17:575\u0026ndash;583.\u003c/li\u003e\n\u003cli\u003eNardone, A., J. A. Casey, R. Morello-Frosch, M. Mujahid, J. R. Balmes, and N. Thakur. 2020. Associations between historical residential redlining and current age-adjusted rates of emergency department visits due to asthma across eight cities in California: an ecological study. The Lancet. Planetary health 4:e24\u0026ndash;e31.\u003c/li\u003e\n\u003cli\u003eNorton, B. A., K. L. Evans, and P. H. Warren. 2016. Urban biodiversity and landscape ecology: Patterns, processes and planning. Current landscape ecology reports 1:178\u0026ndash;192.\u003c/li\u003e\n\u003cli\u003eOksanen, J., F. G. Blanchet, R. Kindt, P. Legendre, P. R. Minchin, R. B. O\u0026rsquo;hara, and Oksanen M. 2013. Package \u0026ldquo;vegan.\u0026rdquo; Community ecology package, version 2:1\u0026ndash;295.\u003c/li\u003e\n\u003cli\u003eParker, K. H., J. M. Bishop, L. E. K. Serieys, R. Mateo, P. R. Camarero, and G. R. M. Leighton. 2023. A heavy burden: Metal exposure across the land-ocean continuum in an adaptable carnivore. Environmental pollution (Barking, Essex: 1987) 327:121-585.\u003c/li\u003e\n\u003cli\u003ePastor, M., R. Morello-Frosch, and J. L. Sadd. 2005. The Air Is Always Cleaner on the Other Side: Race, Space, and Ambient Air Toxics Exposures in California. Journal of urban affairs 27:127\u0026ndash;148.\u003c/li\u003e\n\u003cli\u003ePatten, K. T., A. E. Valenzuela, C. Wallis, E. L. Berg, J. L. Silverman, K. J. Bein, A. S. Wexler, and P. J. Lein. 2021. The effects of chronic exposure to ambient traffic-related air pollution on Alzheimer\u0026rsquo;s disease phenotypes in wildtype and genetically predisposed male and female rats. Environmental health perspectives 129.\u003c/li\u003e\n\u003cli\u003ePetrie, A. 2020. regclass: Tools for an Introductory Class in Regression and Modeling. R package version 1:1\u0026ndash;129.\u003c/li\u003e\n\u003cli\u003ePoisson, M. K. P., F. Gebresenbet, A. R. Butler, P. Tate, D. H. Bergeron, and R. J. Moll. 2024. The way \u0026ldquo;urbanization\u0026rdquo; is defined has strong implications for its effects on mammal abundance. Urban ecosystems:1\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eRamalho, C. E., and R. J. Hobbs. 2012. Time for a change: dynamic urban ecology. Trends in ecology \u0026amp; evolution 27:179\u0026ndash;188.\u003c/li\u003e\n\u003cli\u003eRichards, N. L., G. Tomy, C. A. Kinney, F. C. Nwanguma, B. Godwin, and D. A. (smith) Woollett. 2018. Using Scat Detection Dogs to Monitor Environmental Contaminants in Sentinel Species and Freshwater Ecosystems. Pages 193\u0026ndash;262 \u003cem\u003ein\u003c/em\u003e N. L. Richards, editor. Using Detection Dogs to Monitor Aquatic Ecosystem Health and Protect Aquatic Resources. Springer International Publishing, Cham.\u003c/li\u003e\n\u003cli\u003eRigolon, A. 2016. A complex landscape of inequity in access to urban parks: A literature review. Landscape and urban planning 153:160\u0026ndash;169.\u003c/li\u003e\n\u003cli\u003eRiley, C. B., and M. M. Gardiner. 2020. Examining the distributional equity of urban tree canopy cover and ecosystem services across United States cities. PloS one 15:e0228499.\u003c/li\u003e\n\u003cli\u003eRipley, B., B. Venables, D. M. Bates, K. Hornik, A. Gebhardt, D. Firth, and M. B. Ripley. 2013. Package \u0026ldquo;mass.\u0026rdquo; Cran r 538:113\u0026ndash;120.\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Estival, J., and R. Mateo. 2019. Exposure to anthropogenic chemicals in wild carnivores: a silent conservation threat demanding long-term surveillance. Current opinion in environmental science \u0026amp; health 11:21\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Jorquera, I. A., N. Vitale, L. Garner, D. J. Perez-Venegas, C. J. Galb\u0026aacute;n-Malag\u0026oacute;n, N. Duque-Wilckens, and G. S. Toor. 2017. Contamination of the upper class: Occurrence and effects of chemical pollutants in terrestrial top predators. Current pollution reports 3:206\u0026ndash;219.\u003c/li\u003e\n\u003cli\u003eRosenfeld, C. S. 2017. Gut Dysbiosis in Animals Due to Environmental Chemical Exposures. Frontiers in cellular and infection microbiology 7.\u003c/li\u003e\n\u003cli\u003eSaaristo, M., T. Brodin, S. Balshine, M. G. Bertram, B. W. Brooks, S. M. Ehlman, E. S. McCallum, A. Sih, J. Sundin, B. B. M. Wong, and K. E. Arnold. 2018. Direct and indirect effects of chemical contaminants on the behaviour, ecology and evolution of wildlife. Proceedings. Biological sciences / The Royal Society 285.\u003c/li\u003e\n\u003cli\u003eSanders, D., and K. J. Gaston. 2018. How ecological communities respond to artificial light at night. Journal of experimental zoology. Part A, Ecological and integrative physiology 329:394\u0026ndash;400.\u003c/li\u003e\n\u003cli\u003eSchell, C. J., K. Dyson, T. L. Fuentes, S. Des Roches, N. C. Harris, D. S. Miller, C. A. Woelfle-Erskine, and M. R. Lambert. 2020. The ecological and evolutionary consequences of systemic racism in urban environments. Science 369.\u003c/li\u003e\n\u003cli\u003eSchulte-Hostedde, A. I., Z. Mazal, C. M. Jardine, and J. Gagnon. 2018. Enhanced access to anthropogenic food waste is related to hyperglycemia in raccoons (Procyon lotor). Conservation Physiology 6.\u003c/li\u003e\n\u003cli\u003eSchwarz, K., M. Fragkias, C. G. Boone, W. Zhou, M. McHale, J. M. Grove, J. O\u0026rsquo;Neil-Dunne, J. P. McFadden, G. L. Buckley, D. Childers, L. Ogden, S. Pincetl, D. Pataki, A. Whitmer, and M. L. Cadenasso. 2015. Trees grow on money: urban tree canopy cover and environmental justice. PloS one 10:e0122051.\u003c/li\u003e\n\u003cli\u003eSeebacher, F., and C. E. Franklin. 2012. Determining environmental causes of biological effects: the need for a mechanistic physiological dimension in conservation biology. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 367:1607\u0026ndash;1614.\u003c/li\u003e\n\u003cli\u003eSerieys, L. E. K., A. Lea, M. Epeldegui, J. Foley ,J.G. Moriarty, S.P.D. Riley, C.H. Uittenbogaart, D. Fraser, A. Mouton, and R.K. Wayne. 2018. Widespread anticoagulant poison exposure is linked with immune dysregulation and severe notoedric mange in urban bobcats. Proceedings of the 28th Vertebrate Pest Conference, 258-263.\u003c/li\u003e\n\u003cli\u003eSerieys, L. E. K., A. J. Lea, M. Epeldegui, T. C. Armenta, J. Moriarty, S. VandeWoude, S. Carver, J. Foley, R. K. Wayne, S. P. D. Riley, and C. H. Uittenbogaart. 2018. Urbanization and anticoagulant poisons promote immune dysfunction in bobcats. Proceedings of the Royal Society B: Biological Sciences 285:20172533.\u003c/li\u003e\n\u003cli\u003eShakouri, A., and H. Gheytasi. 2018. Bioaccumulation of heavy metals in oyster (Saccostrea cucullata) from Chabahar bay coast in Oman Sea: Regional, seasonal and size-dependent variations. Marine pollution bulletin 126:323\u0026ndash;329.\u003c/li\u003e\n\u003cli\u003eShkembi, A., L. M. Smith, and R. L. Neitzel. 2024. Linking environmental injustices in Detroit, MI to institutional racial segregation through historical federal redlining. Journal of exposure science \u0026amp; environmental epidemiology 34:389\u0026ndash;398.\u003c/li\u003e\n\u003cli\u003eSievert, O., E. Comley, W. Phiri, and R. S. Davis. 2023. Using camera traps to assess carcass use and the intraguild dynamics of understudied African mesocarnivores. Food Webs 36:e00287.\u003c/li\u003e\n\u003cli\u003eSilva, L. T., A. Magalh\u0026atilde;es, J. F. Silva, and F. Fonseca. 2021. Impacts of low-frequency noise from industrial sources in residential areas. Applied acoustics 182:108203.\u003c/li\u003e\n\u003cli\u003eSmith, W. P., and K. M. Endres. 2012. Raccoon use of den trees and plant associations in western mesophytic forests: Tree attributes and availability or landscape heterogeneity? Natural resources 03:75\u0026ndash;87.\u003c/li\u003e\n\u003cli\u003eSomers, C. M. 2011. Ambient air pollution exposure and damage to male gametes: human studies and in situ \u0026ldquo;sentinel\u0026rdquo; animal experiments. Systems biology in reproductive medicine 57:63\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eSprague, T. S. 2015. Products of Place: The Era of Reinforced-Concrete Skyscrapers in Seattle, 1921-1931. The Pacific Northwest Quarterly 106:107\u0026ndash;119.\u003c/li\u003e\n\u003cli\u003eSugden, S., D. Sanderson, K. Ford, L. Y. Stein, and C. C. St Clair. 2020. An altered microbiome in urban coyotes mediates relationships between anthropogenic diet and poor health. Scientific reports 10:22207.\u003c/li\u003e\n\u003cli\u003eSwope, C. B., D. Hern\u0026aacute;ndez, and L. J. Cushing. 2022. The relationship of historical redlining with present-day neighborhood environmental and health outcomes: A scoping review and conceptual model. Journal of urban health: bulletin of the New York Academy of Medicine 99:959\u0026ndash;983.\u003c/li\u003e\n\u003cli\u003eT\u0026uuml;z\u0026uuml;n, N., B. B. Savaş\u0026ccedil;ı, and R. Stoks. 2021. Seasonal time constraints shape life history, physiology and behaviour independently, and decouple a behavioural syndrome in a damselfly. Oikos 130:274\u0026ndash;286.\u003c/li\u003e\n\u003cli\u003eVlahov, D., N. Freudenberg, F. Proietti, D. Ompad, A. Quinn, V. Nandi, and S. Galea. 2007. Urban as a determinant of health. Journal of urban health: bulletin of the New York Academy of Medicine 84:i16\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eVorhees, D. J., A. C. Cullen, and L. M. Altshul. 1999. Polychlorinated biphenyls in house dust and yard soil near a Superfund site. Environmental science \u0026amp; technology 33:2151\u0026ndash;2156.\u003c/li\u003e\n\u003cli\u003eVoss, R. S., and S. A. Jansa. 2021. Opossums: An adaptive radiation of new world marsupials. Johns Hopkins University Press, Baltimore, MD.\u003c/li\u003e\n\u003cli\u003eWainstein, M., L. B. Harding, S. M. O\u0026rsquo;Neill, D. T. Boyd, F. Koontz, B. Miller, C. F. C. Kl\u0026uuml;tsch, P. J. Thomas, and G. M. Ylitalo. 2022, August 16. Highly contaminated river otters (Lontra canadensis) are effective biomonitors of environmental pollutant exposure. Springer.\u003c/li\u003e\n\u003cli\u003eWang, Y., M. L. Allen, and C. C. Wilmers. 2015. Mesopredator spatial and temporal responses to large predators and human development in the Santa Cruz Mountains of California. Biological conservation 190:23\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eWang, Y., and Z. Cao. 2017. Industrial building environment: Old problem and new challenge. Indoor + built environment: the journal of the International Society of the Built Environment 26:1035\u0026ndash;1039.\u003c/li\u003e\n\u003cli\u003eWingfield, J. C. 2013. Ecological processes and the ecology of stress: the impacts of abiotic environmental factors. Functional ecology 27:37\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eWood, E. M., S. Esaian, C. Benitez, P. J. Ethington, T. Longcore, and L. Y. Pomara. 2024. Historical racial redlining and contemporary patterns of income inequality negatively affect birds, their habitat, and people in Los Angeles, California. Ornithological applications 126:1-29.\u003c/li\u003e\n\u003cli\u003eWorsley-Tonks, K. E. L., E. A. Miller, S. D. Gehrt, S. C. McKenzie, D. A. Travis, T. J. Johnson, and M. E. Craft. 2020. Characterization of antimicrobial resistance genes in Enterobacteriaceae carried by suburban mesocarnivores and locally owned and stray dogs. Zoonoses and public health 67:460\u0026ndash;466.\u003c/li\u003e\n\u003cli\u003eWright, W. J. 2021. As above, so below: Anti‐black violence as environmental racism. Antipode 53:791\u0026ndash;809.\u003c/li\u003e\n\u003cli\u003eYamane, H., S. Tomonaga, R. Suenaga, D. M. Denbow, and M. Furuse. 2007. Intracerebroventricular injection of glutathione and its derivative induces sedative and hypnotic effects under an acute stress in neonatal chicks. Neuroscience letters 418:87\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eYauk, C. L., G. A. Fox, B. E. McCarry, and J. S. Quinn. 2000. Induced minisatellite germline mutations in herring gulls (Larus argentatus) living near steel mills. Mutation research 452:211\u0026ndash;218.\u003c/li\u003e\n\u003cli\u003eYearby, R. 2020. Structural Racism and Health Disparities: Reconfiguring the Social Determinants of Health Framework to Include the Root Cause. The Journal of law, medicine \u0026amp; ethics: a journal of the American Society of Law, Medicine \u0026amp; Ethics 48:518\u0026ndash;526.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 7 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"urban-ecosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ueco","sideBox":"Learn more about [Urban Ecosystems](https://www.springer.com/journal/11252)","snPcode":"11252","submissionUrl":"https://submission.nature.com/new-submission/11252/3","title":"Urban Ecosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"urban ecology, camera traps, pollution, environmental contamination, mesocarnivores, multi-city","lastPublishedDoi":"10.21203/rs.3.rs-6229535/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6229535/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnvironmental factors controlling the distribution and abundance of wildlife populations in the Anthropocene are increasingly complicated by historical and ongoing industrialization. The legacy of industrialization has enduring impacts on environmental quality, with downstream consequences for wildlife. However, industrial contaminants are not evenly distributed across or within cities, and their effects on free-ranging wildlife at the population and community levels remain poorly understood. We investigated whether environmental contamination risk from industrial pollutants was associated with mammalian diversity and carnivore activity in the Seattle-Tacoma metropolitan area, Washington, USA, a historically industrialized region. Using camera trap data collected across 78 sites, we modeled environmental contamination risk, natural land cover, and human population density against several mammal community metrics and against activity rates of carnivore species. We found that mammalian diversity and evenness decreased as contamination risk increased, especially in Seattle. Across the metro area and within Seattle, coyote activity was negatively associated with contamination risk, while raccoon activity was positively associated with contamination risk; opossums showed no response. Within Tacoma, contaminant risk was not significantly associated with mammal community metrics or carnivore activity, but human population density had a negative influence on coyote activity and a positive influence on opossum activity. Our results highlight the impacts of industrialization in ecological processes, and the need for species- and city-specific approaches in understanding the role they play in shaping urban wildlife communities. Ecological studies that incorporate these impacts can inform urban planning and conservation strategies that improve environmental quality for urban wildlife populations.\u003c/p\u003e","manuscriptTitle":"Environmental contamination predicts mammal diversity and mesocarnivore activity in the Seattle- Tacoma metro area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 11:28:24","doi":"10.21203/rs.3.rs-6229535/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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