Human densities, not pollution, affect urban coyote boldness and exploration

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Estien, Lauren A. Stanton, Christopher J. Schell This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5868687/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Comparative studies show that urban coyotes behave differently from their rural counterparts. However, these studies often treat cities as homogeneous. Cities feature diverse pressures for wildlife, such as variable human densities and environmental hazards, two factors that are known to drive increased risk-taking. Thus, this heterogeneity creates a shifting landscape of risk, which may drive locally adapted behavioral strategies within cities. Yet, the influence of these urban pressures on coyote behavior is not well understood. To investigate this, we conducted novel object testing at 24 sites across gradients of human density and pollution. We recorded coyote detections and coyote behavioral responses to the novel object, focusing on time spent alert, time spent close, and total exploration. We found that coyote detections varied with both human density and pollution, with coyote detections being markedly lower in areas with high human density and high pollution. Coyote boldness (time spent alert and close) and exploration were uniformly associated with human density, with coyotes in human-dense displaying elevated boldness and heightened exploration. Our results suggest that urban heterogeneity in human density impacts apex predator behavior, potentially having downstream consequences on human-carnivore coexistence. Biological sciences/Zoology/Animal behaviour Earth and environmental sciences/Ecology/Urban ecology Biological sciences/Ecology/Behavioural ecology Canis latrans risk-taking novel object urbanization carnivore behavior detections Figures Figure 1 Figure 2 Figure 3 1. Introduction Cities are complex, coupled human-nature systems that establish variable landscapes of risk that pressure wildlife to rapidly adjust or face local extirpation 1 – 3 . Because urban ecosystems challenge organisms with novel scenarios and disturbances that encourage phenotypic differentiation, research to date has focused on understanding how urban and rural populations differ. In particular, understanding how urban and rural individuals contrast in behavior, including boldness, an individual’s response to a risky situation or event, and exploration, an individual’s willingness to explore a novel situation, has been of large interest. This is because behavior is a particularly flexible trait that organisms alter in response to environmental cues 4 , 5 . Indeed, urban individuals have been shown to diverge from their rural counterparts in several behaviors, including boldness, exploration, and aggression 3 , 6 . Yet, this research approach has often treated cities as homogeneous, despite the strong landscape heterogeneity within and across cities 7 , 8 . Within-city variation in disturbances, including humans and pollutants, may therefore also drive phenotypic divergence at a fine scale, perhaps even across neighborhoods 9 . Human presence is typically cited as one of the most prominent factors driving alterations in wildlife ecology, including behavior 10 . For instance, urban wildlife are less likely to flee from humans compared to their rural counterparts 11 – 13 , and this heightened tolerance is often used as a metric of boldness 14 , 15 . However, human presence can be further delineated into two distinct features of urban environments that vary across neighborhoods: the “human footprint” (i.e., components of the built environment such as building density) and “human activity” (i.e., population size and foot traffic) 16 . Human footprint and activity can have differential effects on wildlife behavior, with larger carnivores, such as mountain lions ( Puma concolor ) and bobcats ( Lynx rufus ), reducing activity in, or avoiding, areas with high human presence 16 , 17 . Indeed, humans can mediate trophic interactions and shape predator assemblages across the urban matrix, allowing prey and smaller predators to avoid predation by selecting habitats with higher human densities or development 16 , 18 , 19 ; this phenomenon is called the human shield hypothesis 20 . However, organisms in areas of high human activity face trade-offs: their access to natural habitat is relatively limited, with increased exposures to disturbances like noise pollution and environmental contaminants. Relaxed predation pressures and reduced natural habitat may therefore combine to bolster boldness and exploration in human-dominated environments 21 , 22 , leading to the exploitation of anthropogenic resources for food and shelter. Variation in human footprint and activity within cities, as well as species-specific differences toward those disturbances, should induce behavioral variance across neighborhoods and species in a single city 9 . In addition to the amount of natural habitat, the overall quality of habitat can also influence animal behavior. In cities, environmental quality is typically measured by the concentration of pollutants and contaminants (e.g., PM 2.5 , pesticides, heavy metals) 23 , which can have downstream consequences on wildlife behavior 24 , 25 . Environmental pollutants and contaminants are particularly pervasive in cities, with detrimental consequences for organismal health and fitness 25 . For instance, research has linked exposures to air toxins and metals such as chromium to DNA damage, elevated mutation rates, and heightened cortisol levels 26 – 28 . Moreover, most human-produced pollutants are endocrine disruptors, altering the hormonal systems that are known to underpin behavioral traits like boldness, exploration, and activity 29 – 32 . Rats exposed to high levels of diesel exhaust had poorer spatial memory and were less likely to recognize novel objects 33 – 35 . Birds exposed to metal pollution were slower explorers, had altered migratory behavior, and reduced song repertoires 36 – 39 . Fish exposed to environmental contaminants were generally bolder and exhibited slower or decreased exploration tendencies 40 . Thus, contaminants can alter the behavioral traits of an individual that are critical in establishing and succeeding within cities, including boldness and exploration 25 , 29 , 41 , 42 . Changes in behavior may scale up to affect community-level processes 25 , with emerging work demonstrating that areas with greater environmental contamination have lower mammalian biodiversity and impacts mesocarnivore activity 43 . However, the effect of contamination on wildlife ecology may vary by the city 43 , and contamination can also have asymmetrical effects on species as a consequence of sensitivity to certain pollutants or differences in exposure 25 . Urban mesocarnivores, such as coyotes ( Canis latrans ), are an ideal model for investigating urban-induced behavioral changes 44 . Urban coyotes, an apex predator in cities and flagship species for human-carnivore conflict, are bolder and more exploratory than non-urban coyotes 45 , 46 . The behavioral adjustments observed in urban coyotes (e.g., diet flexibility 47 , movement strategies 48 have greatly facilitated their adaptation to urban contexts across biomes and human disturbance regimes 44 ; yet those same shifts have exacerbated human-carnivore conflict 49 – 51 . Though myriad studies have examined how coyotes are adapting to cities behaviorally along axes of urbanization, the effect of contamination on coyote behavior remains uninvestigated (but see Hentati et al. 43 ). This is particularly pressing as due to their role as an apex predator, coyotes are particularly vulnerable to the effects of biomagnification 52 , 53 and disruptions to carnivore behavior via ingestion or exposure to contaminants can alter community dynamics and ecosystem function by disrupting the top-down pressures carnivores exert on other wildlife. Hence, disentangling how human disturbances shape carnivore behavior is critical to maintaining healthy, functioning ecosystems. To address these gaps, we used novel object testing to elicit a behavioral response 45 , 54 , 55 and explore how human density and pollution were associated with coyote risk-taking behaviors (i.e., boldness and exploration). We also explored how the number of coyote detections varied with our landscape variables. We hypothesized that coyote risk-taking would vary with human density and pollution. Specifically, we predicted that high human densities would be associated with elevated boldness and exploration as previous work has demonstrated carnivores in human-dense environments show increased trait values for both behaviors 45 , 56 – 59 . We also predicted that areas with high pollution would be associated with elevated boldness and increased exploration in both coyotes based on work in fish 40 , 60 . Lastly, we predicted that coyote detections would be lowest in human-dense and polluted areas 43 . 2. Methods 2.1 Study Area This research was conducted at 15 public parks and 9 private residences across the East Bay region of Northern California between December 2022 and February 2023. The study included sites within the cities of Antioch (2022 estimated population density: 3,906 people per square mile), Berkeley (12,401 ppl/mi²), Oakland (7,725 ppl/mi²), and Richmond (3,758 ppl/mi²) 61 (Fig. 1 ). The East Bay features a Mediterranean climate, characterized by mild temperatures throughout the year and wet, cooler winters—the conditions during the study period (mid-winter average high of 55°F and low of 42°F) 62 . Motion-activated trail cameras (Bushnell, Overland Park, Kansas, USA) were used at each location and operated for an average of 66 days. Each camera recorded 30-second clips with a one-second interval between triggers. Cameras were deployed only at locations where landowner consent was secured, with all sites spaced at least 1 km apart to align with territorial ranges typical of urban mammals, as established in previous studies 63 – 65 . 2.2 Geospatial Processing and Data Analysis We created a buffer around each camera site at 500m for extracting our landscape variables of human population density 66 and pollution 67 . Due to a minimum camera spacing of 1km, we placed a 500m buffer to avoid potential spatial pseudo-replication between sites (similar to Lombardi et al. 68 , for example). For pollution, we followed the methods in Estien et al. 69 and downloaded environmental hazard variables from CalEnviroScreen 67 to create a pollution burden score for each site. We extracted the mean for the following variables: pm 2.5, diesel pm 2.5, toxic air contaminants, cleanup sites (i.e., brownfield sites), groundwater threat, hazardous sites, solid waste sites, lead risk from housing, traffic, and ozone. We then created a percentile such that a score of 0 would represent no pollution burden and 100 would represent a high pollution burden 69 . We also considered the percentage of impervious surfaces as well as median household income, but these variables were highly correlated with our variables (Figure S1 ); therefore, we excluded these variables. After extracting the mean for human population density and pollution per site buffer (Figure S2), we calculated the median in our dataset and subsequently categorized each site as ‘low’ or ‘high’ per variable. All geospatial analyses were conducted in ArcGIS Pro using the ‘Zonal Statistics’ tool to extract the mean for all hazards. 2.3 Novel Object Test and Behavioral Coding We used a paired-site design where each site ( n = 24) served as both a control and treatment, with the order of condition randomized across sites. At each site, the testing period lasted at least 8 weeks and followed the methods in Breck et al. 45 . Briefly, we dug a shallow hole in the ground and then filled it with a tablespoon of bait (Sweet Meat Predator Bait, Russ Carman, New Milford, Pennsylvania). The hole was then covered with the removed substrate. We placed a fatty acid scent tab (Pocatello Supply Depot, Idaho) on top of the covered hole as an additional attractant 45 . Instances where only the bait and fatty acid tab were applied as described served as our control (Fig. 2 A). For our treatment, we added a novel object consisting of four wood stakes in a 1m 2 square formation around the hole, with a single white rope threaded across the top of all stakes standing roughly 1m above the ground (Fig. 2 B) 45 . For the first four weeks, a site was randomly given the control (only bait) or the treatment (bait + novel object). For the last four weeks, if the site had first received the control, it then received the treatment, and vice versa. For each site, we coded several behaviors of interest (Table S1 ) and recorded the number of detections per species. For detections, videos within 30 minutes of each other were removed to ensure the independence of species triggers (i.e., temporal independence) 43 , 70 . To understand coyote responses to our novel object, we extracted data from each video (i.e., an observation) using Behavior Observation Research Interactive Software (BORIS) 71 . Any video that fell within 30 seconds of the previous video was considered the same observation with the same individuals. Within an observation, we recorded the time spent alert (i.e., being attentive to the surrounding environment) regardless of an individual’s distance to the object. We recorded our remaining behaviors within one body length of the object (see Table S1 for Ethogram). When a species was within one body length of the object, we recorded the amount of time it spent close (i.e., being within one body length of the novel object). Time spent close also included time spent within the object's interior (i.e., between the wood stakes). We consider time spent close and time spent alert as our metrics of boldness. We then measured coyote exploration by recording the number of occurrences of the following behaviors: digging, sniffing, touching, and moving through the object. We then calculated the total number of times an exploratory behavior occurred per observation (i.e., total exploration). Thus, we identified bolder animals as ones that spent less time alert and more time close, and more exploratory animals as ones that have a higher total exploration metric. Prior to coding, four observers were trained on the same 35 videos that we chose at random until > 80% interobserver reliability was achieved. We used Cohen’s kappa to assess interobserver reliability 72 , which was 85.67%. After coding was complete, behavioral data were cleaned to ensure the absence of behaviors (e.g., a coyote was not observed to be alert) was also captured (Supplemental Materials 1). In total, we recorded 313 behavioral observations for coyotes (Table 1 ), with 199 during the control and 114 while the novel object was present. Table 1 Coyote detections in relation to human density and pollution. Variable Number of sites Number of sites visited by coyotes Total visits of coyotes Total Behavioral Observations Low human population density 12 8 226 255 High human population density 12 6 48 58 Low pollution 13 10 123 269 High pollution 11 4 150 44 2.4 Data Analysis We conducted a preliminary analysis via linear mixed models and found that the testing condition order (i.e., control then object, or object then control) did not significantly affect the behaviors we coded for; however, observation number did, showing a strong negative effect on each coyote behavior. We also found that testing condition order did not impact the total number of detections observed at a site (Welch’s t-test p = 0.884), nor did we find a strong relationship between the number of days active and detections (Pearson’s correlation 0.119; Figure S3). Thus, we only included observation number as a fixed effect across all of our final models to control for the negative effect of time, which we expect was due to loss of potency, or complete removal, of the bait over time (e.g., dissipation, consumption, moving of bait by squirrel). To test whether human density and pollution burden were associated with observed behaviors, we used zero-inflated negative binomial mixed models in the glmmTMB package to account for the high number of zeros in our dataset 73 . We used a model selection approach to assess the suitability of various combinations of fixed effects, including testing condition, human density, pollution burden, and observation number. Our null model had observation number as the only fixed effect. Site was included as a random effect across all models. Model fit was assessed using Akaike's information criterion (AIC) corrected for small sample size 74 . Models within two ΔAICc were considered to be equally as likely as our best-performing model. From the top model, we extracted estimates (β), p-values, confidence intervals (CIs), and R-squared goodness of fit values (R 2 ). To compare the behavioral responses between variables in our models, we conducted Tukey’s pairwise comparisons using estimated marginal means from the best-performing model. Lastly, we investigated how coyote detections varied across our landscape variables. To determine if human density and pollution burden had a significant effect on the total number of coyote detections at a site, we used a negative binomial model in the glmmTMB package 73 . Human density and pollution were fixed effects, site was a random effect, and we included the number of days the camera was active per site as an offset variable, following similar methods to Hentati et al. 43 . 3. Results 3.1 Detections Across 24 sites, we received 274 coyote detections in total across 14 sites (Table 1 ). Human densities negatively affected coyote detections (ꞵ = -1.625, CI: -3.056, -0.194, p < 0.05; Figure S4; Table S2), with 48 detections in high human density areas compared to 226 detections in low human density areas. Similarly, pollution negatively affected coyote detections (ꞵ = -3.031, CI: -4.991, -1.071, p < 0.01; Figure S4; Table S2), with 238 and 36 detections in sites with high and low pollution burden, respectively. 3.2 Boldness and Exploration Our top model for time spent alert was our human density and treatment model and held most of the support (weight = 0.79). Models with the greatest support for time spent alert indicated that human density and treatment interacted to affect the amount of time coyotes spend alert (Table 2 ; S3). Generally, coyotes decreased their time spent alert in areas with high human density, and while the novel object was deployed (Table 2 ). When considering the interaction between human density and treatment, we had three findings. First, during the control period (i.e., attractant only), we found that coyotes spent significantly more time alert at sites with low human density (ꞵ = 1.497, p < 0.01; Fig. 3 A). Second, we found that during the object period (i.e., novel object and attractant, we found no significant differences between time spent alert between sites with low and high human density (Fig. 3 A). Lastly, when we compared behavioral responses to each treatment per human density category, we found that coyotes at sites with low human density were significantly less alert during the object period relative to the control (ꞵ = 0.791, p < 0.01), whereas coyotes at high human density sites showed no significant differences (Fig. 3 A). Table 2 Parameter estimates for best-performing coyote risk-taking with human disturbances models. Significant terms are bolded. Behavior Model R 2 c Term Estimate SE Pr (>|z|) 95% CI Time Spent Alert Human Population Density * Treatment 0.370 Intercept 3.468 0.146 < 0.001 3.182, 3.753 Human Density (High) -1.497 0.404 < 0.001 -2.288, -0.705 Treatment (Object) -0.792 0.213 < 0.001 -1.209, -0.375 Observation Number -0.043 0.009 < 0.001 -0.061, -0.247 Human Density (High) * Treatment (Object) 0.976 0.496 < 0.05 0.004, 0.497 Time Spent Close Global 0.531 Intercept 3.822 0.153 < 0.001 3.522, 4.123 Pollution Burden (High) 0.460 0.270 0.089 -0.070, 0.990 Treatment (Object) -1.990 0.253 < 0.001 -2.486, -1.494 Human Density (High) -1.452 0.313 < 0.001 -2.065, -0.839 Observation Number -0.065 0.008 < 0.001 -0.082, -0.049 Pollution (High) * Treatment (Object) -1.260 0.665 0.058 -2.564, 0.044 Human Density (High) * Treatment (Object) 1.466 0.464 < 0.01 0.557, 2.374 Exploration Global 0.456 Intercept 0.997 0.222 < 0.001 0.561, 1.433 Human Density (High) -1.008 0.435 < 0.05 -1.861, -0.154 Treatment (Object) -2.306 0.350 < 0.001 -2.993, -1.620 Observation Number -0.043 0.011 < 0.001 -0.065, -0.022 Human Density (High) * Treatment (Object) 1.337 0.628 < 0.05 0.106, 2.568 The top two models for time spent close were our global model (weight = 0.54) and our human density-treatment interaction model (weight = 0.46), providing strong support for the effect of human density and treatment on this behavior (Table 2 ). We found identical trends for time spent close observed in time spent alert, with coyotes generally spending less time close while the novel object was present and in areas with high human density (Table 2 ). During the control period, we found that coyotes spent significantly more time close at sites with low human density (ꞵ = 1.452, p < 0.001; Fig. 3 B). In contrast, during the object period, we found no significant differences between time spent close between sites with low and high human density (Fig. 3 B). When we compared behavioral responses to each treatment per human density category, we found that coyotes at sites with low human density were significantly less close during the object period relative to the control (ꞵ = 2.620, p < 0.001), whereas coyotes at high human density sites showed no significant differences (Fig. 3 B). Lastly, for total exploration, our top models were the human density-treatment model (weight = 0.60) and treatment model (weight = 0.26), indicating that both human density and treatment strongly affect coyote exploration. We found similar trends in exploration across human density as seen in time spent alert and close, with coyotes exploring less while the novel object was deployed and at sites with high human density (Table 2 ). When we compared exploration during the control period, we did not find any differences in coyote exploration (Fig. 3 C). Similarly, we did not find any differences in coyote exploration when we compared the object period. Finally, when we compared the control and novel object period, we found that coyotes at sites with low human density explored significantly less during the object period relative to the control (ꞵ = 2.306, p < 0.001), whereas coyotes at high human density sites showed no significant differences (Fig. 3 B). 4. Discussion Here, we provided evidence suggesting that within-city variation in human densities and pollution affect coyote ecology. First, we found that the number of coyote detections varied with both human density and pollution, with fewer coyote detections being observed in areas with high human density and high pollution. We also found that pollution had a stronger negative effect on coyote detections than human density, suggesting that habitat quality may be a better predictor of coyote activity than human density. Second, we found that coyote boldness and exploration are uniformly associated with human density. These results suggest that factors associated with human density, such as risk and habituation, drive coyote risk-taking rather than pollution burden. Overall, our results suggest that human densities and pollution differently affect aspects of coyote behavioral ecology, offering insight into how coyotes are adapting to urban environments. We found that coyote detections were markedly lower in areas with higher human densities and higher pollution, similar to emerging research by Hentati et al. 43 . Although coyotes are highly adapted to cities, both social and ecological factors limit their population size, including human-coyote conflict that can lead to lethal removal and finite habitat and territory availability 75 . First, despite the high potential reward of food or other resources (e.g., space, water), areas of high human densities create societal tension for coyotes. Though areas of high human density may have more green space for recreation and other human activities, coyotes that inhabit those greenspaces face a higher probability of conflict and removal 51 , 75 . In contrast, high human density areas can have a high concentration of people and buildings, creating finite prey resources and priming situations for potential negative human-coyote interactions to occur – such as raiding trash cans, moving through yards for food, or extremes like attacking pets. In parallel, ecological resources can similarly limit coyotes in urban areas. Within our study area, ecological resources that coyotes rely on for successfully establishing a territory, such as green space availability, are not evenly distributed as a result of legacies of injustice 76 . Redlining in particular, a policy that denied credit and financial services to individuals based on ethno-racial identity 77 , 78 , has been linked to reduced environmental quality (e.g., less vegetation, high pollution), reduced biodiversity, and altered species assemblages in California 69 , 79 . These areas also have barriers, such as the 580 and 880 highways, that may impede the dispersal and movement of coyotes from greenspaces and richer habitats into areas with higher pollution. Thus, as a consequence of injustice, coyotes in our study area are detected less in areas with more pollution due to the lack of available green space, reduced vegetation, and movement barriers. Our work builds upon previous literature suggesting that urban coyotes are bolder than rural conspecifics 45 , 56 , with boldness varying as a function of human presence 46 , 58 , 59 . We found strong support for our human density hypothesis in coyotes. First, at high human density sites, coyote alertness and proximity did not differ between treatments, whereas at sites with low human density, coyotes spent less time close and alert while the novel object was present. Second, in the control condition, coyotes inhabiting low-density areas spent more time alert and close to the attractant, suggesting heightened wariness of human presence. Coyotes are customarily wary of novel stimuli in both field and captive settings 80 – 82 . However, individuals with greater experiences of people in both the wild 56 and captivity 83 display greater tolerance of human presence and infrastructure. In addition, increased consumption of human food subsidies in cities is thought to promote coyote boldness via associative learning 84 , 85 , which may reduce fear of humans and associated disturbances (e.g., objects, sounds) 56 , 83 , 86 , 87 . Additionally, the absence of apex predators (e.g., mountain lions and wolves) that might otherwise temper risky behaviors could also encourage coyote boldness in cities 21 , 88 . Consistent coyote responses across our treatment conditions in human-dense areas provide additional evidence to support the claim that increased human densities are potentially driving coyote tolerance to human-induced novelty. We also found that coyote exploration, as measured by total exploratory behaviors, is driven by human density. Urban coyotes are known to be more exploratory than their rural counterparts 85 , and if urban coyotes have an extensive history of exploring novel features due to a higher frequency of encountering novelty, coyotes at sites with high human densities may be demonstrating habituation to novelty. For instance, coyotes in areas with high human densities may be exposed more frequently to stimuli such as trash cans, fences, scents, and human infrastructure, compared to coyotes in areas of lower human density. Our additional finding that coyotes at sites with high human density do not significantly change the number of exploratory behaviors directed towards the novel object, unlike coyotes at sites with low human density which reduced their exploration, further supports this hypothesis. However, further research is needed to explicitly investigate if coyotes increase or decrease their exploration when exposed to novel stimuli repeatedly (but see Garcia, Parsons, and Young 89 and Young, Touzot, and Brummer 90 ). Surprisingly, we did not detect an effect of pollution on coyote risk-taking for the above behaviors. Though current work in birds 38 , 39 and fish 40 demonstrate that individuals who face greater exposure to pollutants have altered risk-taking, the relationship between pollution and risk-taking is far from consistent 91 – 93 . For example, although urban great tits were slower explorers at sites with more metal pollution, aggression and nest defense showed no relationship to metal pollution 38 . Similarly, neophobia was unrelated to toxic metal exposure but was more related to urban disturbances 91 . We may not have detected an effect of pollution on coyote behavior due to the coarseness of our environmental hazard variables at the census tract level. Coyotes may be exposed to, for example, air pollutants such as pm 2.5 in their local habitat which is known to vary at very fine spatial scales 94 , 95 . Hence, to disentangle the effect of environmental contamination on coyote behavior within cities, a better approach may be to sample locally via air quality monitors and soil samples to understand site-level exposure to pollutants. Similarly, other relevant contaminants, such as rodenticides, pesticides, and heavy metals, were not included in our pollution metric and may have adverse impacts on coyote behavior 25 . Our results point to coyotes in high human density areas exhibiting elevated boldness and heightened exploration. These findings point to several potential mechanisms to infer how the coyote behaviors we documented vary as a function of human density. First, habituation or learning may be driving elevated risk-taking in areas of high human density. In areas with more people, coyotes are exposed to a myriad of novel physical, visual, and olfactory stimuli. Through repeated exposure, coyote responses to risky stimuli (e.g., novel objects, loud noises) diminish leading to no marked differences between the absence of a novel object and the introduction of one as seen in our study. Second, these findings point to differences in the perception of overall risk between low and high human density areas. Areas of high human density have reduced predator assemblages, which can promote boldness and exploration 21 , 22 . Hence, the absence of natural predators, namely mountain lions, primes coyotes to behave more with more risk by, for example, coyotes in human-dense areas not spending as much time alert during our control. Additionally, coyotes in high human density areas encounter risk more frequently than other conspecifics, including vehicles, people, and other disturbances associated with cities. Hence, because coyotes must mitigate risk frequently, they are able to assess and determine the threat of novelty and adjust behaviors if necessary. In our study, this is reflected by human-dense coyotes not adjusting their behaviors in the face of novelty, unlike their non-human-dense counterparts who reduced their risk-taking when presented with novelty via reductions in exploration and time spent close. Lastly, development is also a salient factor that could be driving our behavioral observations. Captive research has shown that bold coyotes produce litters that are even bolder 83 and thus, coyote pups that are raised by bold parents likely have personality traits that yield riskier behaviors. Regardless of the mechanism, our results suggest that behavioral strategies in coyotes differ across urban landscapes as a function of people, rather than being monotonic. In summary, we provided evidence to suggest marked differences in coyote behavioral traits within cities. We demonstrated that coyote detections across the landscape as a function of pollution and human densities, while coyote risk-taking varied with differences in human density. Further research is needed to disentangle the exact mechanisms that lead to changes in behavioral strategies, and if these strategies are consistent across other urban mesocarnivores. Our results provide critical insight into urban coyote behavioral ecology, creating a foundation to further explore how intra-city variation influences traits that predict individual success in urban areas, and what these behavioral changes mean for potential downstream consequences for human-carnivore interactions in cities. Declarations Author Contribution COE and CJS designed the research project. COE and LAS conducted fieldwork. COE led the coding of videos. COE led statistical analyses, with support from LAS and CJS. COE wrote the first draft of the manuscript. COE made the figures. COE, LAS, and CJS edited the manuscript. Acknowledgement C.O.E. was supported by the University of California, Berkeley’s Chancellor Fellowship and the National Science Foundation Graduate Research Fellowship under Grant No. DGE-2146752. LAS was supported by the National Science Foundation Postdoctoral Research Fellowship in Biology under Grant No. DBI-2305981. We thank the Carol Baird Student Award for Field Research for supporting this research. We thank the undergraduates involved in the work – Esteban Contreras, Vishal Subramanyan, and Nicole Cantangay – for helping score videos. This research was performed on the traditional lands of the Ohlone, Karkin, Miwok, Muwekma, Yukots & Confederated Villages of Lisjan. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funders. Data Availability The data analyzed for the study are available from the corresponding author on reasonable request. References Fischer, J. D., Schneider, S. C., Ahlers, A. A. & Miller, J. R. Categorizing wildlife responses to urbanization and conservation implications of terminology. Conserv. Biol. 29 , 1246–1248 (2015). Hahs, A. K. et al. Urbanisation generates multiple trait syndromes for terrestrial animal taxa worldwide. Nat. 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The Weather Year Round Anywhere on Earth. https://weatherspark.com/ Gallo, T., Fidino, M., Lehrer, E. W. & Magle, S. B. Mammal diversity and metacommunity dynamics in urban green spaces: implications for urban wildlife conservation. Ecol. Appl. 27 , 2330–2341 (2017). Magle, S. B. et al. Wealth and urbanization shape medium and large terrestrial mammal communities. Glob. Change Biol. 27 , 5446–5459 (2021). Haight, J. D. et al. Urbanization, climate and species traits shape mammal communities from local to continental scales. Nat. Ecol. Evol. 7 , 1654–1666 (2023). U.S. Census Bureau. Annual Estimates of the Resident Population for the United States, Regions, States, District of Columbia, and Puerto Rico: April 1, 2020 to July 1, 2022 (NST-EST2022-POP). (2022). https://www.census.gov/data/tables/time-series/demo/popest/2020s-state-total.html California Office of Environmental Health Hazard Assessment. CalEnviroScreen 4.0. (2021). https://oehha.ca.gov/calenviroscreen/report/draft-calenviroscreen-40 Lombardi, J. V., Comer, C. E., Scognamillo, D. G. & Conway, W. C. Coyote, fox, and bobcat response to anthropogenic and natural landscape features in a small urban area. Urban Ecosyst. 20 , 1239–1248 (2017). Estien, C. O., Wilkinson, C. E., Morello-Frosch, R. & Schell, C. J. Historical redlining is associated with disparities in environmental quality across California. Environ. Sci. Technol. Lett. 11 , 54–59 (2024). Mills, K. L. & Harris, N. C. Humans disrupt access to prey for large African carnivores. Elife 9 , e60690 (2020). Friard, O. & Gamba, M. BORIS: a free, versatile open-source event‐logging software for video/audio coding and live observations. Methods Ecol. Evol. 7 , 1325–1330 (2016). Gisev, N., Bell, J. S. & Chen, T. F. Interrater agreement and interrater reliability: key concepts, approaches, and applications. Res. Social Administrative Pharm. 9 , 330–338 (2013). Magnusson, A. et al. 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Eapen, A. P. Human-Wildlife Coexistence with Coyotes in Los Angeles County, CA and Cook County, Il (State University of New York at Albany, 2022). Timm, R. M. & Baker, R. O. A history of urban coyote problems. Proceedings 12th Wildlife Damage Management Conference 272–286 (2007). Prugh, L. R. et al. The rise of the mesopredator. Bioscience 59 , 779–791 (2009). Garcia, A. C., Parsons, M. A. & Young, J. K. Effects of early-life experience on innovation and problem-solving in captive coyotes. Behav. Ecol. Sociobiol. 76 , 141 (2022). Young, J. K., Touzot, L. & Brummer, S. P. Persistence and conspecific observations improve problem-solving abilities of coyotes. PloS one . 14 , e0218778 (2019). Grunst, A. S., Grunst, M. L., Pinxten, R. & Eens, M. Personality and plasticity in neophobia levels vary with anthropogenic disturbance but not toxic metal exposure in urban great tits: urban disturbance, metal pollution and neophobia. Sci. Total Environ. 656 , 997–1009 (2019). Kobiela, M. 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Supplementary Files CoyoteMSSupplementalInfo.docx Cite Share Download PDF Status: Published Journal Publication published 30 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Apr, 2025 Reviews received at journal 14 Mar, 2025 Reviewers agreed at journal 07 Mar, 2025 Reviews received at journal 07 Mar, 2025 Reviewers agreed at journal 28 Feb, 2025 Reviewers invited by journal 04 Feb, 2025 Editor assigned by journal 27 Jan, 2025 Editor invited by journal 23 Jan, 2025 Submission checks completed at journal 22 Jan, 2025 First submitted to journal 20 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5868687","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":407696141,"identity":"ebddb047-6cd6-4cb2-820a-296ef7a36d59","order_by":0,"name":"Cesar O. Estien","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYLCCBAYGOQMGBmYGhgIw34CQBsYGoBZjiBYDYrUAicQNRGvhb29//uBBxZ307ey9jw0+GNgkNrA3b5PAp0XizBnDhoQzz3J39hw3TpxhkJbYwHOsDK8WA4kcxobEtsO5G26kMR/mMTic2CCRY4Zfi/zzhw2J/w6nG9x/xnz4j8H/xAb5NwS0SDAYNiQ2HE4wuMHGnMxgcABoCw9+LRJncgxnJBw7bLizJ43ZsMcg2biNJ63YAp8W/vbjDz7+qDksb85+jFniR4WdbD/74Y038GnBAI5tJCkHAXuSdYyCUTAKRsGwBwDs5UxS3tBdYwAAAABJRU5ErkJggg==","orcid":"","institution":"University of California– Berkeley","correspondingAuthor":true,"prefix":"","firstName":"Cesar","middleName":"O.","lastName":"Estien","suffix":""},{"id":407696142,"identity":"7cd71492-d0ea-451c-8001-f8945a1b3b0e","order_by":1,"name":"Lauren A. Stanton","email":"","orcid":"","institution":"University of California– Berkeley","correspondingAuthor":false,"prefix":"","firstName":"Lauren","middleName":"A.","lastName":"Stanton","suffix":""},{"id":407696143,"identity":"5d1c1cb1-ba43-4ee1-8f92-8a8cc6e9cce3","order_by":2,"name":"Christopher J. Schell","email":"","orcid":"","institution":"University of California– Berkeley","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"J.","lastName":"Schell","suffix":""}],"badges":[],"createdAt":"2025-01-20 21:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5868687/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5868687/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-21946-y","type":"published","date":"2025-10-30T15:58:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74955228,"identity":"c16c9030-3249-4867-827a-576c42a4e313","added_by":"auto","created_at":"2025-01-28 17:21:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40169,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area Overview. A map of the experimental sites located in the East Bay Region of the Bay Area in Northern California. Each dot represents a camera.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5868687/v1/f2a305a5c271d0fc51e3f8ac.jpg"},{"id":74955229,"identity":"36b462ee-6740-49f0-9cd3-52afa8ab5dbd","added_by":"auto","created_at":"2025-01-28 17:21:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35843,"visible":true,"origin":"","legend":"\u003cp\u003eImages of coyotes interacting with our treatments. The top image (A) shows a coyote interacting during the control period (attractant only). The bottom image (B) shows a coyote interacting during the novel object period (attractant and novel object).\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5868687/v1/cbe789a2638160b2878bba79.jpg"},{"id":74955233,"identity":"0ba05b7b-8b70-4fbf-9f53-bd05ef730c24","added_by":"auto","created_at":"2025-01-28 17:21:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30907,"visible":true,"origin":"","legend":"\u003cp\u003eCoyote risk-taking in relation to human density and pollution. The top row shows (A) time spent alert, (B) time spent close, and (C) total exploration in relation to human density. The control (novel scent) is shown in white, and the treatment (novel object and scent) is shown in brown. P-values are extracted from pair-wise Tukey comparisons of the estimated marginal means from the best-performing model for each behavior.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5868687/v1/b30cebde324d85850f6be3c8.jpg"},{"id":95040529,"identity":"34fddc6a-6a11-46f9-af9c-9615789c7c8a","added_by":"auto","created_at":"2025-11-03 16:09:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":976861,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5868687/v1/4859fe4d-d1d7-40d3-96ad-fab5d7bc7519.pdf"},{"id":74955231,"identity":"23ada8b5-00f6-4a89-ae0c-4003f478b5cc","added_by":"auto","created_at":"2025-01-28 17:21:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":344017,"visible":true,"origin":"","legend":"","description":"","filename":"CoyoteMSSupplementalInfo.docx","url":"https://assets-eu.researchsquare.com/files/rs-5868687/v1/a738fbd209de8c96d794ba72.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Human densities, not pollution, affect urban coyote boldness and exploration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCities are complex, coupled human-nature systems that establish variable landscapes of risk that pressure wildlife to rapidly adjust or face local extirpation\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Because urban ecosystems challenge organisms with novel scenarios and disturbances that encourage phenotypic differentiation, research to date has focused on understanding how urban and rural populations differ. In particular, understanding how urban and rural individuals contrast in behavior, including boldness, an individual\u0026rsquo;s response to a risky situation or event, and exploration, an individual\u0026rsquo;s willingness to explore a novel situation, has been of large interest. This is because behavior is a particularly flexible trait that organisms alter in response to environmental cues\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Indeed, urban individuals have been shown to diverge from their rural counterparts in several behaviors, including boldness, exploration, and aggression\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Yet, this research approach has often treated cities as homogeneous, despite the strong landscape heterogeneity within and across cities \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Within-city variation in disturbances, including humans and pollutants, may therefore also drive phenotypic divergence at a fine scale, perhaps even across neighborhoods\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHuman presence is typically cited as one of the most prominent factors driving alterations in wildlife ecology, including behavior\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. For instance, urban wildlife are less likely to flee from humans compared to their rural counterparts\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and this heightened tolerance is often used as a metric of boldness\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, human presence can be further delineated into two distinct features of urban environments that vary across neighborhoods: the \u0026ldquo;human footprint\u0026rdquo; (i.e., components of the built environment such as building density) and \u0026ldquo;human activity\u0026rdquo; (i.e., population size and foot traffic)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Human footprint and activity can have differential effects on wildlife behavior, with larger carnivores, such as mountain lions (\u003cem\u003ePuma concolor\u003c/em\u003e) and bobcats (\u003cem\u003eLynx rufus\u003c/em\u003e), reducing activity in, or avoiding, areas with high human presence\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Indeed, humans can mediate trophic interactions and shape predator assemblages across the urban matrix, allowing prey and smaller predators to avoid predation by selecting habitats with higher human densities or development\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e; this phenomenon is called the human shield hypothesis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, organisms in areas of high human activity face trade-offs: their access to natural habitat is relatively limited, with increased exposures to disturbances like noise pollution and environmental contaminants. Relaxed predation pressures and reduced natural habitat may therefore combine to bolster boldness and exploration in human-dominated environments\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, leading to the exploitation of anthropogenic resources for food and shelter. Variation in human footprint and activity within cities, as well as species-specific differences toward those disturbances, should induce behavioral variance across neighborhoods and species in a single city\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to the amount of natural habitat, the overall quality of habitat can also influence animal behavior. In cities, environmental quality is typically measured by the concentration of pollutants and contaminants (e.g., PM\u003csub\u003e2.5\u003c/sub\u003e, pesticides, heavy metals)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, which can have downstream consequences on wildlife behavior\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Environmental pollutants and contaminants are particularly pervasive in cities, with detrimental consequences for organismal health and fitness\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. For instance, research has linked exposures to air toxins and metals such as chromium to DNA damage, elevated mutation rates, and heightened cortisol levels\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Moreover, most human-produced pollutants are endocrine disruptors, altering the hormonal systems that are known to underpin behavioral traits like boldness, exploration, and activity\u003csup\u003e\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Rats exposed to high levels of diesel exhaust had poorer spatial memory and were less likely to recognize novel objects\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Birds exposed to metal pollution were slower explorers, had altered migratory behavior, and reduced song repertoires\u003csup\u003e\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Fish exposed to environmental contaminants were generally bolder and exhibited slower or decreased exploration tendencies\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Thus, contaminants can alter the behavioral traits of an individual that are critical in establishing and succeeding within cities, including boldness and exploration\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Changes in behavior may scale up to affect community-level processes\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, with emerging work demonstrating that areas with greater environmental contamination have lower mammalian biodiversity and impacts mesocarnivore activity\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. However, the effect of contamination on wildlife ecology may vary by the city\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, and contamination can also have asymmetrical effects on species as a consequence of sensitivity to certain pollutants or differences in exposure\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUrban mesocarnivores, such as coyotes (\u003cem\u003eCanis latrans\u003c/em\u003e), are an ideal model for investigating urban-induced behavioral changes\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Urban coyotes, an apex predator in cities and flagship species for human-carnivore conflict, are bolder and more exploratory than non-urban coyotes\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The behavioral adjustments observed in urban coyotes (e.g., diet flexibility\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, movement strategies\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e have greatly facilitated their adaptation to urban contexts across biomes and human disturbance regimes\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e; yet those same shifts have exacerbated human-carnivore conflict\u003csup\u003e\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Though myriad studies have examined how coyotes are adapting to cities behaviorally along axes of urbanization, the effect of contamination on coyote behavior remains uninvestigated (but see Hentati et al.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e). This is particularly pressing as due to their role as an apex predator, coyotes are particularly vulnerable to the effects of biomagnification\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e and disruptions to carnivore behavior via ingestion or exposure to contaminants can alter community dynamics and ecosystem function by disrupting the top-down pressures carnivores exert on other wildlife. Hence, disentangling how human disturbances shape carnivore behavior is critical to maintaining healthy, functioning ecosystems.\u003c/p\u003e \u003cp\u003eTo address these gaps, we used novel object testing to elicit a behavioral response\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e and explore how human density and pollution were associated with coyote risk-taking behaviors (i.e., boldness and exploration). We also explored how the number of coyote detections varied with our landscape variables. We hypothesized that coyote risk-taking would vary with human density and pollution. Specifically, we predicted that high human densities would be associated with elevated boldness and exploration as previous work has demonstrated carnivores in human-dense environments show increased trait values for both behaviors\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan additionalcitationids=\"CR57 CR58\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. We also predicted that areas with high pollution would be associated with elevated boldness and increased exploration in both coyotes based on work in fish\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Lastly, we predicted that coyote detections would be lowest in human-dense and polluted areas\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThis research was conducted at 15 public parks and 9 private residences across the East Bay region of Northern California between December 2022 and February 2023. The study included sites within the cities of Antioch (2022 estimated population density: 3,906 people per square mile), Berkeley (12,401 ppl/mi\u0026sup2;), Oakland (7,725 ppl/mi\u0026sup2;), and Richmond (3,758 ppl/mi\u0026sup2;)\u003csup\u003e61\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The East Bay features a Mediterranean climate, characterized by mild temperatures throughout the year and wet, cooler winters\u0026mdash;the conditions during the study period (mid-winter average high of 55\u0026deg;F and low of 42\u0026deg;F)\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Motion-activated trail cameras (Bushnell, Overland Park, Kansas, USA) were used at each location and operated for an average of 66 days. Each camera recorded 30-second clips with a one-second interval between triggers. Cameras were deployed only at locations where landowner consent was secured, with all sites spaced at least 1 km apart to align with territorial ranges typical of urban mammals, as established in previous studies\u003csup\u003e\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Geospatial Processing and Data Analysis\u003c/h2\u003e \u003cp\u003eWe created a buffer around each camera site at 500m for extracting our landscape variables of human population density\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e and pollution\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Due to a minimum camera spacing of 1km, we placed a 500m buffer to avoid potential spatial pseudo-replication between sites (similar to Lombardi et al.\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, for example). For pollution, we followed the methods in Estien et al.\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e and downloaded environmental hazard variables from CalEnviroScreen\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e to create a pollution burden score for each site. We extracted the mean for the following variables: pm 2.5, diesel pm 2.5, toxic air contaminants, cleanup sites (i.e., brownfield sites), groundwater threat, hazardous sites, solid waste sites, lead risk from housing, traffic, and ozone. We then created a percentile such that a score of 0 would represent no pollution burden and 100 would represent a high pollution burden\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. We also considered the percentage of impervious surfaces as well as median household income, but these variables were highly correlated with our variables (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e); therefore, we excluded these variables. After extracting the mean for human population density and pollution per site buffer (Figure S2), we calculated the median in our dataset and subsequently categorized each site as \u0026lsquo;low\u0026rsquo; or \u0026lsquo;high\u0026rsquo; per variable. All geospatial analyses were conducted in ArcGIS Pro using the \u0026lsquo;Zonal Statistics\u0026rsquo; tool to extract the mean for all hazards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Novel Object Test and Behavioral Coding\u003c/h2\u003e \u003cp\u003eWe used a paired-site design where each site (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24) served as both a control and treatment, with the order of condition randomized across sites. At each site, the testing period lasted at least 8 weeks and followed the methods in Breck et al.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Briefly, we dug a shallow hole in the ground and then filled it with a tablespoon of bait (Sweet Meat Predator Bait, Russ Carman, New Milford, Pennsylvania). The hole was then covered with the removed substrate. We placed a fatty acid scent tab (Pocatello Supply Depot, Idaho) on top of the covered hole as an additional attractant\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Instances where only the bait and fatty acid tab were applied as described served as our control (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). For our treatment, we added a novel object consisting of four wood stakes in a 1m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e square formation around the hole, with a single white rope threaded across the top of all stakes standing roughly 1m above the ground (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. For the first four weeks, a site was randomly given the control (only bait) or the treatment (bait\u0026thinsp;+\u0026thinsp;novel object). For the last four weeks, if the site had first received the control, it then received the treatment, and vice versa. For each site, we coded several behaviors of interest (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and recorded the number of detections per species. For detections, videos within 30 minutes of each other were removed to ensure the independence of species triggers (i.e., temporal independence)\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo understand coyote responses to our novel object, we extracted data from each video (i.e., an observation) using Behavior Observation Research Interactive Software (BORIS)\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Any video that fell within 30 seconds of the previous video was considered the same observation with the same individuals. Within an observation, we recorded the time spent alert (i.e., being attentive to the surrounding environment) regardless of an individual\u0026rsquo;s distance to the object. We recorded our remaining behaviors within one body length of the object (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for Ethogram). When a species was within one body length of the object, we recorded the amount of time it spent close (i.e., being within one body length of the novel object). Time spent close also included time spent within the object's interior (i.e., between the wood stakes). We consider time spent close and time spent alert as our metrics of boldness. We then measured coyote exploration by recording the number of occurrences of the following behaviors: digging, sniffing, touching, and moving through the object. We then calculated the total number of times an exploratory behavior occurred per observation (i.e., total exploration). Thus, we identified bolder animals as ones that spent less time alert and more time close, and more exploratory animals as ones that have a higher total exploration metric.\u003c/p\u003e \u003cp\u003ePrior to coding, four observers were trained on the same 35 videos that we chose at random until \u0026gt;\u0026thinsp;80% interobserver reliability was achieved. We used Cohen\u0026rsquo;s kappa to assess interobserver reliability\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, which was 85.67%. After coding was complete, behavioral data were cleaned to ensure the absence of behaviors (e.g., a coyote was not observed to be alert) was also captured (Supplemental Materials 1). In total, we recorded 313 behavioral observations for coyotes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with 199 during the control and 114 while the novel object was present.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoyote detections in relation to human density and pollution.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of sites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of sites visited by coyotes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal visits of coyotes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal Behavioral Observations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow human population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh human population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Analysis\u003c/h2\u003e \u003cp\u003eWe conducted a preliminary analysis via linear mixed models and found that the testing condition order (i.e., control then object, or object then control) did not significantly affect the behaviors we coded for; however, observation number did, showing a strong negative effect on each coyote behavior. We also found that testing condition order did not impact the total number of detections observed at a site (Welch\u0026rsquo;s t-test \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.884), nor did we find a strong relationship between the number of days active and detections (Pearson\u0026rsquo;s correlation 0.119; Figure S3). Thus, we only included observation number as a fixed effect across all of our final models to control for the negative effect of time, which we expect was due to loss of potency, or complete removal, of the bait over time (e.g., dissipation, consumption, moving of bait by squirrel).\u003c/p\u003e \u003cp\u003eTo test whether human density and pollution burden were associated with observed behaviors, we used zero-inflated negative binomial mixed models in the \u003cem\u003eglmmTMB\u003c/em\u003e package to account for the high number of zeros in our dataset\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. We used a model selection approach to assess the suitability of various combinations of fixed effects, including testing condition, human density, pollution burden, and observation number. Our null model had observation number as the only fixed effect. Site was included as a random effect across all models. Model fit was assessed using Akaike's information criterion (AIC) corrected for small sample size\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Models within two ΔAICc were considered to be equally as likely as our best-performing model. From the top model, we extracted estimates (β), p-values, confidence intervals (CIs), and R-squared goodness of fit values (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e). To compare the behavioral responses between variables in our models, we conducted Tukey\u0026rsquo;s pairwise comparisons using estimated marginal means from the best-performing model.\u003c/p\u003e \u003cp\u003eLastly, we investigated how coyote detections varied across our landscape variables. To determine if human density and pollution burden had a significant effect on the total number of coyote detections at a site, we used a negative binomial model in the \u003cem\u003eglmmTMB\u003c/em\u003e package\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Human density and pollution were fixed effects, site was a random effect, and we included the number of days the camera was active per site as an offset variable, following similar methods to Hentati et al.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Detections\u003c/h2\u003e \u003cp\u003eAcross 24 sites, we received 274 coyote detections in total across 14 sites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Human densities negatively affected coyote detections (ꞵ = -1.625, CI: -3.056, -0.194, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Figure S4; Table S2), with 48 detections in high human density areas compared to 226 detections in low human density areas. Similarly, pollution negatively affected coyote detections (ꞵ = -3.031, CI: -4.991, -1.071, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Figure S4; Table S2), with 238 and 36 detections in sites with high and low pollution burden, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Boldness and Exploration\u003c/h2\u003e \u003cp\u003eOur top model for time spent alert was our human density and treatment model and held most of the support (weight\u0026thinsp;=\u0026thinsp;0.79). Models with the greatest support for time spent alert indicated that human density and treatment interacted to affect the amount of time coyotes spend alert (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; S3). Generally, coyotes decreased their time spent alert in areas with high human density, and while the novel object was deployed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When considering the interaction between human density and treatment, we had three findings. First, during the control period (i.e., attractant only), we found that coyotes spent significantly more time alert at sites with low human density (ꞵ = 1.497, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Second, we found that during the object period (i.e., novel object and attractant, we found no significant differences between time spent alert between sites with low and high human density (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Lastly, when we compared behavioral responses to each treatment per human density category, we found that coyotes at sites with low human density were significantly less alert during the object period relative to the control (ꞵ = 0.791, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas coyotes at high human density sites showed no significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameter estimates for best-performing coyote risk-taking with human disturbances models. Significant terms are bolded.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePr (\u0026gt;|z|)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime Spent Alert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman Population Density * Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.182, 3.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman Density (High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.288, -0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTreatment (Object)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.209, -0.375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObservation Number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.061, -0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman Density (High) * Treatment (Object)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004, 0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime Spent Close\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.522, 4.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePollution Burden (High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.070, 0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTreatment (Object)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.486, -1.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman Density (High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.065, -0.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObservation Number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.082, -0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePollution (High) * Treatment (Object)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.564, 0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman Density (High) * Treatment (Object)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.557, 2.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExploration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.561, 1.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman Density (High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.861, -0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTreatment (Object)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.993, -1.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObservation Number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.065, -0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman Density (High) * Treatment (Object)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.106, 2.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe top two models for time spent close were our global model (weight\u0026thinsp;=\u0026thinsp;0.54) and our human density-treatment interaction model (weight\u0026thinsp;=\u0026thinsp;0.46), providing strong support for the effect of human density and treatment on this behavior (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We found identical trends for time spent close observed in time spent alert, with coyotes generally spending less time close while the novel object was present and in areas with high human density (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During the control period, we found that coyotes spent significantly more time close at sites with low human density (ꞵ = 1.452, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In contrast, during the object period, we found no significant differences between time spent close between sites with low and high human density (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). When we compared behavioral responses to each treatment per human density category, we found that coyotes at sites with low human density were significantly less close during the object period relative to the control (ꞵ = 2.620, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas coyotes at high human density sites showed no significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eLastly, for total exploration, our top models were the human density-treatment model (weight\u0026thinsp;=\u0026thinsp;0.60) and treatment model (weight\u0026thinsp;=\u0026thinsp;0.26), indicating that both human density and treatment strongly affect coyote exploration. We found similar trends in exploration across human density as seen in time spent alert and close, with coyotes exploring less while the novel object was deployed and at sites with high human density (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When we compared exploration during the control period, we did not find any differences in coyote exploration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Similarly, we did not find any differences in coyote exploration when we compared the object period. Finally, when we compared the control and novel object period, we found that coyotes at sites with low human density explored significantly less during the object period relative to the control (ꞵ = 2.306, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas coyotes at high human density sites showed no significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHere, we provided evidence suggesting that within-city variation in human densities and pollution affect coyote ecology. First, we found that the number of coyote detections varied with both human density and pollution, with fewer coyote detections being observed in areas with high human density and high pollution. We also found that pollution had a stronger negative effect on coyote detections than human density, suggesting that habitat quality may be a better predictor of coyote activity than human density. Second, we found that coyote boldness and exploration are uniformly associated with human density. These results suggest that factors associated with human density, such as risk and habituation, drive coyote risk-taking rather than pollution burden. Overall, our results suggest that human densities and pollution differently affect aspects of coyote behavioral ecology, offering insight into how coyotes are adapting to urban environments.\u003c/p\u003e \u003cp\u003eWe found that coyote detections were markedly lower in areas with higher human densities and higher pollution, similar to emerging research by Hentati et al.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Although coyotes are highly adapted to cities, both social and ecological factors limit their population size, including human-coyote conflict that can lead to lethal removal and finite habitat and territory availability\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. First, despite the high potential reward of food or other resources (e.g., space, water), areas of high human densities create societal tension for coyotes. Though areas of high human density may have more green space for recreation and other human activities, coyotes that inhabit those greenspaces face a higher probability of conflict and removal\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. In contrast, high human density areas can have a high concentration of people and buildings, creating finite prey resources and priming situations for potential negative human-coyote interactions to occur \u0026ndash; such as raiding trash cans, moving through yards for food, or extremes like attacking pets. In parallel, ecological resources can similarly limit coyotes in urban areas. Within our study area, ecological resources that coyotes rely on for successfully establishing a territory, such as green space availability, are not evenly distributed as a result of legacies of injustice\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Redlining in particular, a policy that denied credit and financial services to individuals based on ethno-racial identity\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, has been linked to reduced environmental quality (e.g., less vegetation, high pollution), reduced biodiversity, and altered species assemblages in California\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. These areas also have barriers, such as the 580 and 880 highways, that may impede the dispersal and movement of coyotes from greenspaces and richer habitats into areas with higher pollution. Thus, as a consequence of injustice, coyotes in our study area are detected less in areas with more pollution due to the lack of available green space, reduced vegetation, and movement barriers.\u003c/p\u003e \u003cp\u003eOur work builds upon previous literature suggesting that urban coyotes are bolder than rural conspecifics\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, with boldness varying as a function of human presence\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. We found strong support for our human density hypothesis in coyotes. First, at high human density sites, coyote alertness and proximity did not differ between treatments, whereas at sites with low human density, coyotes spent less time close and alert while the novel object was present. Second, in the control condition, coyotes inhabiting low-density areas spent more time alert and close to the attractant, suggesting heightened wariness of human presence. Coyotes are customarily wary of novel stimuli in both field and captive settings\u003csup\u003e\u003cspan additionalcitationids=\"CR81\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. However, individuals with greater experiences of people in both the wild\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e and captivity\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e display greater tolerance of human presence and infrastructure. In addition, increased consumption of human food subsidies in cities is thought to promote coyote boldness via associative learning\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e,\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, which may reduce fear of humans and associated disturbances (e.g., objects, sounds)\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e,\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. Additionally, the absence of apex predators (e.g., mountain lions and wolves) that might otherwise temper risky behaviors could also encourage coyote boldness in cities\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. Consistent coyote responses across our treatment conditions in human-dense areas provide additional evidence to support the claim that increased human densities are potentially driving coyote tolerance to human-induced novelty.\u003c/p\u003e \u003cp\u003eWe also found that coyote exploration, as measured by total exploratory behaviors, is driven by human density. Urban coyotes are known to be more exploratory than their rural counterparts\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, and if urban coyotes have an extensive history of exploring novel features due to a higher frequency of encountering novelty, coyotes at sites with high human densities may be demonstrating habituation to novelty. For instance, coyotes in areas with high human densities may be exposed more frequently to stimuli such as trash cans, fences, scents, and human infrastructure, compared to coyotes in areas of lower human density. Our additional finding that coyotes at sites with high human density do not significantly change the number of exploratory behaviors directed towards the novel object, unlike coyotes at sites with low human density which reduced their exploration, further supports this hypothesis. However, further research is needed to explicitly investigate if coyotes increase or decrease their exploration when exposed to novel stimuli repeatedly (but see Garcia, Parsons, and Young\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e and Young, Touzot, and Brummer\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eSurprisingly, we did not detect an effect of pollution on coyote risk-taking for the above behaviors. Though current work in birds\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and fish\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e demonstrate that individuals who face greater exposure to pollutants have altered risk-taking, the relationship between pollution and risk-taking is far from consistent\u003csup\u003e\u003cspan additionalcitationids=\"CR92\" citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. For example, although urban great tits were slower explorers at sites with more metal pollution, aggression and nest defense showed no relationship to metal pollution\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Similarly, neophobia was unrelated to toxic metal exposure but was more related to urban disturbances\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. We may not have detected an effect of pollution on coyote behavior due to the coarseness of our environmental hazard variables at the census tract level. Coyotes may be exposed to, for example, air pollutants such as pm 2.5 in their local habitat which is known to vary at very fine spatial scales\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e,\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. Hence, to disentangle the effect of environmental contamination on coyote behavior within cities, a better approach may be to sample locally via air quality monitors and soil samples to understand site-level exposure to pollutants. Similarly, other relevant contaminants, such as rodenticides, pesticides, and heavy metals, were not included in our pollution metric and may have adverse impacts on coyote behavior\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur results point to coyotes in high human density areas exhibiting elevated boldness and heightened exploration. These findings point to several potential mechanisms to infer how the coyote behaviors we documented vary as a function of human density. First, habituation or learning may be driving elevated risk-taking in areas of high human density. In areas with more people, coyotes are exposed to a myriad of novel physical, visual, and olfactory stimuli. Through repeated exposure, coyote responses to risky stimuli (e.g., novel objects, loud noises) diminish leading to no marked differences between the absence of a novel object and the introduction of one as seen in our study. Second, these findings point to differences in the perception of overall risk between low and high human density areas. Areas of high human density have reduced predator assemblages, which can promote boldness and exploration\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Hence, the absence of natural predators, namely mountain lions, primes coyotes to behave more with more risk by, for example, coyotes in human-dense areas not spending as much time alert during our control. Additionally, coyotes in high human density areas encounter risk more frequently than other conspecifics, including vehicles, people, and other disturbances associated with cities. Hence, because coyotes must mitigate risk frequently, they are able to assess and determine the threat of novelty and adjust behaviors if necessary. In our study, this is reflected by human-dense coyotes not adjusting their behaviors in the face of novelty, unlike their non-human-dense counterparts who reduced their risk-taking when presented with novelty via reductions in exploration and time spent close. Lastly, development is also a salient factor that could be driving our behavioral observations. Captive research has shown that bold coyotes produce litters that are even bolder\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e and thus, coyote pups that are raised by bold parents likely have personality traits that yield riskier behaviors. Regardless of the mechanism, our results suggest that behavioral strategies in coyotes differ across urban landscapes as a function of people, rather than being monotonic.\u003c/p\u003e \u003cp\u003eIn summary, we provided evidence to suggest marked differences in coyote behavioral traits within cities. We demonstrated that coyote detections across the landscape as a function of pollution and human densities, while coyote risk-taking varied with differences in human density. Further research is needed to disentangle the exact mechanisms that lead to changes in behavioral strategies, and if these strategies are consistent across other urban mesocarnivores. Our results provide critical insight into urban coyote behavioral ecology, creating a foundation to further explore how intra-city variation influences traits that predict individual success in urban areas, and what these behavioral changes mean for potential downstream consequences for human-carnivore interactions in cities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCOE and CJS designed the research project. COE and LAS conducted fieldwork. COE led the coding of videos. COE led statistical analyses, with support from LAS and CJS. COE wrote the first draft of the manuscript. COE made the figures. COE, LAS, and CJS edited the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eC.O.E. was supported by the University of California, Berkeley\u0026rsquo;s Chancellor Fellowship and the National Science Foundation Graduate Research Fellowship under Grant No. DGE-2146752. LAS was supported by the National Science Foundation Postdoctoral Research Fellowship in Biology under Grant No. DBI-2305981. We thank the Carol Baird Student Award for Field Research for supporting this research. We thank the undergraduates involved in the work \u0026ndash; Esteban Contreras, Vishal Subramanyan, and Nicole Cantangay \u0026ndash; for helping score videos. This research was performed on the traditional lands of the Ohlone, Karkin, Miwok, Muwekma, Yukots \u0026amp; Confederated Villages of Lisjan. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funders.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data analyzed for the study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFischer, J. 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Technol.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 7564\u0026ndash;7573 (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Canis latrans, risk-taking, novel object, urbanization, carnivore behavior, detections","lastPublishedDoi":"10.21203/rs.3.rs-5868687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5868687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eComparative studies show that urban coyotes behave differently from their rural counterparts. However, these studies often treat cities as homogeneous. Cities feature diverse pressures for wildlife, such as variable human densities and environmental hazards, two factors that are known to drive increased risk-taking. Thus, this heterogeneity creates a shifting landscape of risk, which may drive locally adapted behavioral strategies within cities. Yet, the influence of these urban pressures on coyote behavior is not well understood. To investigate this, we conducted novel object testing at 24 sites across gradients of human density and pollution. We recorded coyote detections and coyote behavioral responses to the novel object, focusing on time spent alert, time spent close, and total exploration. We found that coyote detections varied with both human density and pollution, with coyote detections being markedly lower in areas with high human density and high pollution. Coyote boldness (time spent alert and close) and exploration were uniformly associated with human density, with coyotes in human-dense displaying elevated boldness and heightened exploration. Our results suggest that urban heterogeneity in human density impacts apex predator behavior, potentially having downstream consequences on human-carnivore coexistence.\u003c/p\u003e","manuscriptTitle":"Human densities, not pollution, affect urban coyote boldness and exploration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 17:21:08","doi":"10.21203/rs.3.rs-5868687/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-04T13:03:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-14T14:31:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110324842001371053445854818231200471027","date":"2025-03-07T21:44:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-07T16:23:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187106003012698582967159892223029036651","date":"2025-02-28T16:42:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-04T13:42:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-27T13:32:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-23T15:41:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-22T12:00:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-01-20T21:17:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf653891-276d-4421-b780-21c737330532","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43468566,"name":"Biological sciences/Zoology/Animal behaviour"},{"id":43468567,"name":"Earth and environmental sciences/Ecology/Urban ecology"},{"id":43468568,"name":"Biological sciences/Ecology/Behavioural ecology"}],"tags":[],"updatedAt":"2025-11-03T16:05:51+00:00","versionOfRecord":{"articleIdentity":"rs-5868687","link":"https://doi.org/10.1038/s41598-025-21946-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-30 15:58:30","publishedOnDateReadable":"October 30th, 2025"},"versionCreatedAt":"2025-01-28 17:21:08","video":"","vorDoi":"10.1038/s41598-025-21946-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-21946-y","workflowStages":[]},"version":"v1","identity":"rs-5868687","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5868687","identity":"rs-5868687","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}