Identifying the Factors That Influence Raccoon (Procyon Lotor) and Southeastern Myotis (Myotis Austroriparius) Use of Stormwater Sewer Systems

Identifying the Factors That Influence Raccoon (Procyon Lotor) and Southeastern Myotis (Myotis Austroriparius) Use of Stormwater Sewer Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identifying the Factors That Influence Raccoon (Procyon Lotor) and Southeastern Myotis (Myotis Austroriparius) Use of Stormwater Sewer Systems Alan A. Ivory II, Matt T. Hallett, Miguel A. Acevedo, Brett R. Scheffers, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6355575/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 May, 2025 Read the published version in Urban Ecosystems → Version 1 posted 12 You are reading this latest preprint version Abstract Wildlife living within human-dominated and/or modified landscapes may explore and use unconventional habitats. Our study investigates the overlooked potential of stormwater sewer systems (SSSs) as habitat for two urban-dwelling species: raccoons ( Procyon lotor ) and southeastern myotis bats ( Myotis austroriparius ). Here we focus specifically on the construction-based factors that most greatly affect the occupancy of these two species within the SSS of Alachua Co., Florida. With many vertebrates using SSSs for movement, foraging, and roosting, knowing what factors influence a system's usability is important when designing urban corridors. Our findings suggest that raccoon occupancy in SSSs was most closely related to the proximity to the nearest exit, but bats seem to select roosting sites based on a multitude of factors, including the size of the SSS, the distance to the nearest exit, and the level of impervious surface aboveground. Raccoons have a preference to remain near an exit suggesting that their presence in SSSs may be exploratory or constrained by food or light availability, although they were found navigating the full extent of some SSSs. Myotis a. prefer smaller stormwater systems with limited impervious surface disturbance aboveground, particularly in smaller SSSs. We use these findings to discuss ways that construction design and stormwater management can be more wildlife friendly. Urbanization Stormwater management Occupancy modeling Corridors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The rapid urbanization and expansion of human activities have dramatically transformed landscapes across the globe (Cramer and Portier 2001 ; Peterson 2002 ). Urbanization brings a suite of challenges for wildlife, including habitat fragmentation, altered food sources, and increased noise and light pollution (Messmer 2009 ; Shilling et al. 2018 ; Fehlmann et al. 2021 ). Yet some species have shown resilience and adaptation to these anthropogenic changes (Shochat et al. 2010 ; Fitzgerald et al. 2014 ). A diverse array of vertebrates, from birds to mammals, have managed to exploit urban niches, often capitalizing on the resources inadvertently provided by human activities (Jokimäki et al. 2011 ; Griffin et al. 2017 ). Birds provide some well-known examples, as peregrine falcons ( Falco peregrinus ) make their nests on rooftops, feral pigeons ( Columba livia ) who not only nest on rooftops but also feed on discarded human food (Sacchi et al. 2002 ; Kettel et al. 2019 ), chimney swifts ( Chaetura pelagica ) modify their nesting behavior to exploit man-made structures using chimneys, air shafts, and wells for roosting (Dexter 1969 ; Wheeler 2013 ). Mammals in urban environments are less common, but raccoons ( Procyon lotor ) are known to have smaller home ranges in urbanized areas (Slate 1985 ; Feigley 1992 ; Prange et al. 2004 ) due to increased stability of available resources (Hoffmann and Gottschang 1977 ). These instances represent the plasticity of behavior that some species possess, allowing them to overcome the challenges of urban life. Raccoons are an icon among urban wildlife due to their widespread distribution and frequent close association with humans (Hoffmann and Gottschang 1977 ; McClearn 1992 ; Prange et al. 2003 , 2004 ; Graser III et al. 2012). They often forage in trash bins, on discarded human food, and can capitalize on human structures for shelter (Nixon et al. 2001 ). Similarly, bats, such as big brown bats ( Eptesicus fuscus ) and Rafinesque's big-eared bats ( Corynorhinus rafinesquii ), have carved out a unique niche in urban ecosystems by utilizing buildings as roosting sites and benefiting from the abundance of insects attracted to urban lights (Rydell 1992 ; Duchamp et al. 2004 ; Ferrara and Leberg 2005 ; Gehrt and Chelsvig 2008 ). These examples highlight the flexible behaviors and ecological preferences that enable these creatures to bridge the gap between their natural habitats and the anthropogenic urban landscape. To better navigate human-modified environments, some vertebrates have taken advantage of designed eco-tunnels and wildlife crossings (Jackson and Griffin 2000 ; Forman 2003 ; Glista et al. 2009 ). Typically, these wildlife crossings are straight culverts, with the purpose of facilitating animal movements from one side of a road to the other (Philcox et al. 1999 ; Bain et al. 2017 ). Such construction often decreases roadkill and reduces genetic loss due to mortality (Dodd Jr et al. 2004 ; Jackson and Fahrig 2011 ; Sawaya et al. 2014 ). Stormwater sewer systems (SSSs) in the U.S. are structurally similar to the urban passages that are specifically designed for animals to use (Hunt et al. 1987 ; Tran 2016 ; Bain et al. 2017 ). These systems, designed to manage excess rainwater and prevent flooding (Che et al. 2014 ), inadvertently create a network of dark, concealed spaces that may be enticing for animals seeking refuge from urban pressures. Factors such as traffic, noise, and tunnel dimensions are known to influence an animal’s likelihood of using constructed wildlife crossings (Jackson and Griffin 2000 ; Kerth and Melber 2009 ; Nickel et al. 2020 ), and potentially similar factors may influence their probability of using SSSs. We explored the factors influencing raccoon and southeastern myotis ( Myotis austroriparius ) occupancy within the SSSs of north Florida's urban landscape. We used single-species occupancy models (SSOMs), to analyze the relationships among construction-based factors and the presence of these species. To accomplish this goal, we deployed camera traps at numerous locations across Alachua, Co. Florida, USA, and recorded site-specific factors through field sampling and remote sensing. Our findings offer insights into the broader mechanisms by which wildlife adapts to human-altered environments. Additionally, our study can inform the design and maintenance of stormwater systems, considering the potential impacts on the wildlife communities that have become entwined with them. Methods Study Design We conducted the study in Alachua County, Florida, USA, across a range of environmental and construction conditions. Alachua County participates in the Tree City USA program (Berland et al. 2016 ), signifying its status as a developed area with abundant green spaces. To capture diverse conditions, our study area included urban regions, such as downtown Gainesville and the University of Florida campus, along with state highways, and less-developed areas, including neighborhoods, golf courses, and green spaces. Using underground utility and SSS shapefiles provided by the City of Gainesville and the University of Florida Facility Services (UFFS), we selected study sites based on the following criteria: (1) SSS pipes were at least 30 cm in diameter to allow for a camera trap to function properly (McCleery et al. 2014 ), and animal access, (2) located in areas approved by the City of Gainesville, Florida Department of Transportation, or the UFFS, (3) exclusion of locations with damaged or broken pipes, and (4) only one sampling site was used per SSS to maintain sampling independence. At the study’s conclusion, we removed an additional site that lacked sufficient trap-nights to construct a robust occupancy model. This left 33 camera sites in unique “sewersheds” across Alachua County (Fig. 1 ), comprising 12 simple culverts and 21 more complex systems. For the southeastern myotis analysis, we included data from all 33 cameras, while for the raccoon analysis we removed two culvert sites due to water inundation throughout the study and, in one case, a metal barricade at the pipe outlet, which restricted ground entry. Camera-Trap Surveys We installed camera-traps (Bushell Care S-4K No Glow #119949C; Prime Combo Low Glow #119932CB; Bushnell®, KS, USA; Moultrie M-880 #MCG12594 Moultrie®, AL, USA) at each of the 33 sites from February through June 2023 (Fig. 1 ). Where possible, we mounted cameras using a magnetic attachment to the back, providing a parallel view of the bottom of the SSS pipe (Ivory et al. 2024 ). In non-metal SSSs, we attached cameras to a tree at the nearest entrance of the SSS pipe. Due to variations in SSS construction, mounting distances were not fully standardized, but, whenever feasible, cameras were placed about 1 m from the pipe (McCleery et al. 2014 ; Baker 2015 ; Zeitler et al. 2023 ), corresponding to the average depth of SSSs in our study area. We collected 10-second videos for each motion response to improve detection probability, given the close proximity of wildlife to the camera. This setup enabled us to identify most vertebrates to species level, even without a complete view of each individual. Camera-traps were active for a total of 1,921 trap-nights, averaging 58.2 trap nights per site. Variations in trap nights per site were due to water damage affecting five cameras and the theft of two cameras. We conducted biweekly visits to all cameras to download video data and replaced batteries whenever the battery indicator dropped to one bar. Site-Specific Factors As our study is one of the first to determine the drivers of wildlife use of SSSs, at each of the 33 camera sites we measured a broad array of site-specific environmental and construction-based variables that may drive raccoon and bat occupancy within these systems. One-time measurements included the distance from a camera to the nearest exit via a pipe within its sewershed (we did not consider curb inlets as exits), distance from camera to ground (depth), length of the connected stormwater system, impervious surface percentage as a proxy for human disturbance above the system, SSS pipe diameter, number of pipe nodes, pipe availability within 100 m, the number of lanes of traffic of the associated road nearest the camera, and if the SSS crossed under a road (Table 1 ). We measured the distance to the nearest wetland, but this factor was removed from the analysis due to a strong correlation with the distance to the nearest exit (r = 0.86). We also collected information related to the speed limit of the associated road, but this variable was strongly correlated to the nearest road size variable (r = 0.80), so we also removed this variable from the analysis. Table 1 List of covariates used for SSOM Variable name Variable description Variable Type System.Size Length (m) of pipes in the system with a camera trap Continuous Distance to Exit Distance (m) from the camera to the end of the associated SSS pipe. Continuous Pipe.within.100M Length (m) of connected SSS pipe within 100M of the camera trap. Continuous Pipe.Size Pipe Diameter (cm.) of associated SSS. Continuous Nodes Number of pipes intersecting at the camera site. Integer Cross.Road Does the connected SSS pipe cross a road? Binary (Yes/No) Nearest.Road.Size Number of lanes of traffic on the associated roadway. Integer Depth Distance from camera mount to ground/bottom of SSS pipe. Continuous Impervious.Percentage Percentage of 100-meter buffer covered by an impervious surface. Continuous We used the National Land Cover Database (NLCD) 2023 CONUS Fractional Impervious Surface dataset to produce the average percentage of impervious surfaces within 100 meters of each sampling site (Dixon 2012 ; U.S. Geological Survey (USGS) 2024 ). The NLCD Fractional Impervious Surface dataset provides the percentage of ground cover that is classified as impervious, such as pavement and buildings, at the 30-meter resolution. Impervious surface values that fall within a 100 m buffer of each sampling site were averaged. The percentage of the camera sites’ 100 m buffers that were covered by an impervious surface ranged from 4–64%, with an average of 33% of the cameras’ buffer consisting of impervious surfaces (Fig. 2 a). As SSSs are associated with urbanized areas, more complex stormwater systems tended to have a higher average impervious surface percentage (35%) compared to culverts (30%). We measured the distance from a camera to the nearest sewer pipe exit by using the measure distance tool in ArcGIS Pro to trace the shortest route to the desired point (ArcGIS Pro 2024 ). For the distance to the nearest exit measurement, we defined an exit as a pipe outlet, thus excluding curb inlets and grates that varied in size and often precluded animal access. Distances to exits from a camera site varied from 0 to 225 m, with an average of 43 m (Fig. 2 b). A total of nine cameras were categorized as having a 0 m distance to exit. This is because the camera was attached to a tree at the pipe opening or was attached to the opening of the pipe with a magnetic mount. For this reason, seven of the culvert camera sites had a distance to exit of 0 m, with the average distance in culvert SSSs being 8 m. Cameras in complex systems tended to be further from an exit with an average distance of 67 m. The stormwater pipe diameter and the number of pipe nodes at the camera site were derived from information in the attribute table on manhole nodes of stormwater shapefiles from the City of Gainesville and UFFS databases (Bullington 2024 ). The pipe diameter of SSSs used in our study ranged from 30 to 183 cm, with a mean of 74 cm (Fig. 2 c). Complex systems averaged a pipe diameter of 61 cm, while culverts were on average 96 cm. To measure the length of pipes within 100 m of the camera site, we clipped the SSS pipe’s line feature to the camera sites’ 100-meter buffer, where the pipe length that falls within the buffer is summed (Fig. 2 d, Fig. 3 ). Of the camera sites that we used, there was an average of 180 m of pipe available within 100 m of the camera site. Pipe availability ranged from 14 to 537 m. As culverts, by definition, were limited in size, the length of pipe within 100 m of a camera site was less, with an average of 75 m, compared to 240 m for complex systems. We used the Measure Distance tool in ArcGIS Pro to determine the size of a sewershed by tracing the connected SSS pipes associated with the camera site. The length of pipes connected to camera sites ranged from 12 to 6,913 m, with an average summed length of 879 m (Fig. 2 e). Culverts were shorter with an average connected pipe length of 29 m compared to more complex sewersheds with an average system size of 1,366 m. During camera-trap deployment, we measured the distance from the camera to the floor of the pipe within the SSS. To collect this measurement to the nearest cm, we used a tape to measure from the rim of the manhole opening to the pipe floor. The average distance from the camera to the base of the pipe was 1.56 m and ranged from 0.40 to 4.90 m (Fig. 2 f). Many culverts were constructed of concrete, preventing the use of magnetic mounts, leading to the average distance from the camera to the pipe being further than cameras in complex systems. The average distance for culverts was 1.93 m, whereas the cameras in complex SSSs had an average distance to the floor of the pipe of 1.36 m. The number of intersecting pipes at a camera site ranged from 1–4, with an average of 1.9 (Fig. 2 g). A value of 1 indicates that the camera was positioned either at a pipe opening or on a catch basin with only one inlet. This setup allows water to enter from a grate or curb inlet above, but it involves only one drainage pipe. An average value of near 2 indicates that most often the camera trap was at a site with a pipe extending in both directions. As systems are defined as being more complex than culverts, the average number of nodes at these camera sites was 2.3, whereas culverts had an average of 1.3 nodes. Nearly all sewersheds containing a camera crossed under a road (82%; Fig. 2 h). This was slightly higher in complex systems (86%), compared to culverts (75%). While culverts are typically designed to move water from one side of the road to the other, our study included one culvert that crossed under a grass walking trail, another culvert that connected two ponds, and a third culvert crossed under a bike path. We also recorded the number of lanes of traffic on the associated roadway. The SSSs we used in our study had an average of 2.1 lanes of traffic (Fig. 2 i). Complex systems averaged slightly smaller road size with 2 lanes of traffic, compared to 2.3 lanes for culverts. Analysis We used the wildlife observations from our camera-traps and the site-specific construction factors to construct single-species occupancy models (SSOMs; MacKenzie et al. 2002 ). Although we detected a total of 35 species within the SSSs (Ivory et al. 2024 ), we only constructed occupancy models for raccoons and southeastern myotis. These species were chosen as they had the most widespread presence in SSSs, and for the southeastern myotis, their conservation interest as a result to their sensitivity to urbanization and human disturbance (Duchamp et al. 2004 ; Gehrt and Chelsvig 2008 ; Webb et al. 2021 ; Lehrer et al. 2021 ). We constructed SSOMs in R package unmarked , as we are using camera-trap data of unmarked wildlife to track occupancy (Fiske and Chandler 2011 ; Burton et al. 2015 ; Kellner et al. 2023 ; Posit team 2024 ; R Core Team 2024 ). We recorded each wildlife observation individually. As a first step, we grouped species occurrence counts per week by site number, where occupancy for each species was denoted as a one or a zero for each week. To ensure all sites were given equal effort, we used an effort matrix to outline which weeks a site had an active camera for all seven days. Sites with a camera active for less than seven days most often occurred due to dead camera batteries, vegetation overgrowth, or humans tampering with the camera. If a site did not have an active camera for the full week we eliminated that week from the effort matrix, and in turn, the associated observation count data from that week were not used in the occupancy model. We scaled and centered the site-specific covariates to aid the unmarked package in parameter estimation (Saracco et al. 2011 ). The occupancy models with covariates were compared to a “null” (intercept-only) model, with no predictors for detection (p) or occupancy (ψ). Models with covariates included those with no predictor for detection and site-specific covariates as predictors for occupancy. Models with covariates also included models with the distance from the camera to the ground (“Depth”) as a predictor for detection with site-specific covariates as predictors for occupancy (Table 1 ). Covariates were used as predictors for occupancy individually and additively. Results We observed raccoons most often, with a total of 1,755 detections. Southeastern myotis were observed on 418 occasions. Of the 33 (31 for raccoons) camera sites that we used for occupancy models, raccoons were recorded at 21 sites, while southeastern myotis were detected at 15 sites. The number of observations per site exhibited considerable variability for both species. Raccoon observations per site for the extent of our study ranged from 2 to 517 sightings, while bat observations ranged from 1 to 300. Raccoons were detected by most cameras with similar frequencies. Conversely, bat observations were primarily concentrated at three sites, with eight sites each recording only a single observation. Raccoons The most parsimonious model explaining the occupancy of raccoons included distance to exit as a covariate for occupancy and depth as a covariate for the probability of detection (Table 2 ). The second best model ( ΔAIC = 2.07) also included depth as a covariate for the probability of detection, but with both the distance to exit and the nearest road size as predictors of occupancy. Notably, the top five models all included distance to exit as a covariate for occupancy, and the depth of the sewer pipe from the camera was included as a covariate for detection in the top 18 models (Table 2 ). Table 2 AIC table for raccoon SSOMs across all SSSs. p is the probability of detection and ψ is the probability of occupancy Model ΔAIC Weight p(Depth) ψ(Distance.to.Exit) 0.00 0.344 p(Depth) ψ(Distance.to.Exit + Nearest.Road.Size) 2.07 0.122 p(Depth) ψ(Distance.to.Exit + Pipe.Size) 2.17 0.116 p(Depth) ψ(Distance.to.Exit + System.Size) 2.34 0.107 p(Depth) ψ(Distance.to.Exit + Nodes) 2.86 0.082 p(Depth) ψ(Cross.Road) 3.71 0.054 p(Depth) ψ(Impervious.Percentage) 4.73 0.032 p(Depth) ψ(Pipe.Size) 5.42 0.023 p(Depth) ψ(Impervious.Percentage + Pipe.Size) 5.59 0.021 p(Depth) ψ(System.Size) 5.72 0.020 p(Depth) ψ(Cross.Road + Nearest.Road.Size) 6.39 0.014 p(Depth) ψ(Nearest.Road.Size) 6.61 0.013 p(Depth) ψ(Impervious.Percentage + System.Size) 6.77 0.012 p(Depth) ψ(Nodes) 6.86 0.011 p(Depth) ψ(Pipes.within.100M) 7.12 0.010 p(Depth) ψ(Depth) 7.46 0.008 p(Depth) ψ(System.Size + Depth) 8.53 0.005 p(Depth) ψ(Pipes.within.100M + Nodes) 9.57 0.003 p(.) ψ(Distance.to.Exit) 11.63 0.001 p(.) ψ(.) 16.98 0.000 In the best model that included the distance to exit as a covariate for occupancy and depth as a predictor of detection, the probability of detection increased 2.01 (± 0.42 SE) times with a unit increase in Depth (p < 0.001). Additionally, in the top model, the probability of occupancy decreased 0.29 (± 0.15 SE) times with a unit increase in the distance to an exit (p = 0.016; Fig. 4 a). In the second-best model, which included the distance to exit and the nearest road size as a predictor of occupancy, with depth as a predictor of detection, the probability of detection continued to increase 2.01 (± 0.42 SE) times with a unit increase in Depth (p < 0.001). The probability of occupancy continued to decrease 0.29 (± 0.15 SE) times with a unit increase in the distance to exit (p = 0.019), while the odds of occupancy decreased 0.65 (± 0.32 SE) times with a unit increase in the nearest road size (p = 0.377). Based on these results, we would expect less raccoon occupancy further into a system near large roads, although the relationship between road size and raccoon occupancy has greater uncertainty (Fig. 4 b). Bats For bat occupancy models constructed using SSOMs, seven models performed better than the null, with five models within two ΔAIC units (Table 3 ). The top five models did not include any predictors of detection, while three of the top five models did include impervious surface percentage as a detector of occupancy. The top performing model included the sites impervious surface percentage as the sole predictor of occupancy, while the second-best model included the system size and the distance to exit covariates as predictors of occupancy (Table 3 ). The remaining three models with a ΔAIC within two units of the top model includes a model with the impervious surface percentage and the system size as predictors of occupancy, the impervious surface percentage and the pipe size as predictors of occupancy, and a model with the system size as a predictor of occupancy. The best model, using the impervious surface percentage as a predictor of bat occupancy, predicts the odds of bats occupying a SSS decreased 0.39 (± 0.17 SE) times for a unit increase in the impervious surface percentage (p = 0.036; Fig. 5 a). The second-best model, including the system size and the distance to exit from a sampling location, suggests that bat occupancy increased 2.79 (± 2.09 SE) times for a unit increase in the distance to exit (p = 0.171), but the odds of occupying a SSS decreased by 0.13 (± 0.28 SE) times for a unit increase in the system size (p = 0.341; Fig. 5 b). Table 3 AIC table for bat SSOMs across all SSSs Model ΔAIC Weight p(.) ψ(Impervious.Percentage) 0.00 0.166 p(.) ψ(System.Size + Distance.to.Exit) 0.66 0.120 p(.) ψ(Impervious.Percentage + System.Size) 1.16 0.093 p(.) ψ(Impervious.Percentage + Pipe.Size) 1.72 0.070 p(.) ψ(System.Size) 1.78 0.068 p(Depth) ψ(Impervious.Percentage) 2.17 0.056 p(Depth) ψ(Distance.to.Exit + System.Size) 2.54 0.047 p(.)ψ(.) 2.91 0.039 An additional two models within two ΔAIC of the top model that contained the impervious percentage covariant suggested that the odds of bats occupancy in a SSS decreased by 0.46 (± 0.21 SE; p = 0.090) and 0.41 (± 0.18 SE; p = 0.043) times, respectively, for a unit increase in the impervious surface percentage above ground. Similar to the second-best model, the model with the impervious surface percentage and the system size, system size continues to be negatively related to bat occupancy, suggesting that bat occupancy decreased 0.49 (± 0.37 SE) times per unit increase in the system size (p = 0.346; Fig. 5 c). In the model containing the impervious surface percentage and the pipe size as predictors of occupancy, bat occupancy was predicted to have increased 1.52 (± 0.72 SE) times for a unit increase in pipe size (p = 0.377). According to this model, bats are more likely to occupy larger piped SSSs with lower levels of above-ground impervious surfaces (Fig. 5 d). The final model with a ΔAIC less than two was a model with no predictors for detection and the system size as the sole predictor of occupancy, which suggested that bat occupancy decreased 0.35 (± 0.30 SE) times for a unit increase in the system size (p = 0.224; Fig. 5 e). Note that impervious surface had a significant relationship (p < 0.05) with bat occupancy in the best model, and in the model containing the impervious surface and the pipe size, while it did not have a significant relationship in the model containing the impervious surface percentage and the system size. No other covariates had a significant relationship with bat occupancy. Discussion Raccoons Our results show that the probability of raccoon occupancy decreased with an increase in the distance to the nearest exit. While the distance to the nearest exit covariate was included in the top five models, all models with additional covariates performed worse than the model where the distance to an exit was the sole predictor of occupancy. While raccoon occupancy decreased as the distance from an exit in the SSS increased, raccoons are still predicted to travel in systems up to three times further than the average distance to exit of camera sites used in our study. The depth of the sewer system, or the distance between the ground and the camera, is a relevant factor to consider when considering detection of raccoons, as the probability of detection increased two times with a unit increase in the depth covariant. This importance of being near an exit for raccoons may be a response to prey availability in the form of other species that venture into the SSS or fall in from the curb inlets unwittingly. As raccoons have been occasionally observed entering and exiting a SSS using curb inlets, their selection of areas near a pipe outlet may be driven by other species that are restricted to such areas. The aquatic life that we observed raccoons feeding on are likely to remain in proximity to a pipe outlet due to the presence of associated water bodies (Le Viol et al. 2009 ; Ivory et al. 2024 ; McKercher et al. 2024 ). Consequently, this proximity to water bodies may encourage raccoons to stay in these areas as well. Additionally, light available from curb inlets may be insufficient, promoting more activity near a pipe outlet. Our results found that raccoon occupancy declined 0.65 times with a unit increase in the nearest road size. The number of lanes of traffic is influenced by traffic volume and determines the width of road. This in part determines the degree of fragmentation of the habitat above ground (Prange et al. 2004 ). Along with the negative relationship raccoons may have with vehicles as it relates to a risk of roadkill, traffic noise may deter them from using a nearby SSSs (Shilling et al. 2018 ). Of the 31 sampling sites we monitored, 23 were adjacent to a two-laned road. This may contribute to some of the uncertainty in our findings, as the distance to the nearest road covariant deviated from the average. The majority of our study took place in Gainesville, Florida, a medium-sized college town, where two-lane roads are the norm, except for major highways that pass through the city. Raccoons are generalists and respond well to human disturbance (Graser III et al. 2012). The percentage of impervious surface covariant, which was used as a proxy for human disturbance, did not perform well, with a ΔAIC of 4.73 and a weight of 0.032. This suggests that the level of human disturbance was not an important factor when considering a SSS's suitability for raccoons. At the site with the most impervious surfaces (64%), 27 of the 191 raccoon observations were recorded from 12:00 PM to 5:00 PM, when foot traffic above the grate was assumed to be at its highest. For a primarily nocturnal species (Rooney et al. 2025 ), this level of raccoon presence with high levels of human activity is one such example of why human disturbance may not be important for raccoons considering using an SSS. Bats While there was not a clear top model for bat occupancy, the percentage of impervious surfaces at a site was a common covariate across three of the top five models within two ΔAIC. Our results suggest that bat occupancy decreased ~ 0.42 times for a unit increase in impervious surfaces. The system size covariant, also included in three of the top five occupancy models, suggested that an increase in the system size decreases the odds of bat occupancy by about 0.32 times. Bats are well documented in the U.S. roosting under bridges and culverts (Goehring 1957 ; Keeley and Tuttle 1999 ; Leivers et al. 2019 ). Such locations are typically in areas with limited impervious surfaces, along rural roads, and would be below the average SSS size used in our study. Our findings are in line with Dixon ( 2012 ), who found that insectivorous bat activity is negatively related to impervious surfaces; although impervious surfaces were not included as a predictor in the top models for Myotis sp. in their study. Of the 418 bat observations we documented at sites included in the SSOMs, only 16 observations occurred at a culvert site. By comparison, smaller complex SSSs were frequently used by bats. While the role of SSSs as habitat for bats is difficult to fully capture based on camera trap footage, we observed bats both roosting on manhole covers, where a camera was attached, and flying within the sewer pipes, traveling within the SSSs and feeding on insects. The second-best model included the system size covariate additively with the distance to exit covariate as predictors of bat occupancy. Seemingly, bats prefer to be further from an exit, but not in too large of an SSS. This may in part be due to increased noise near roads. Roosting sites closer to an exit may be exposed to more noise and less preferable to bats (Kerth and Melber 2009 ). Being far from a road in a relatively small system may minimize noise disturbance and not be as large of an energy investment as using a larger SSS. Seasonality may be an important unaccounted-for factor when creating these occupancy models (Goehring 1957 ; Gore and Studenroth Jr 2005 ). While our study included some of the colder months in northern Florida, a more complete record of potential winter hibernation is crucial. It is also important to note that at the sites where we documented the most bats, the camera would be triggered hundreds of times a day, draining batteries sooner and, in turn, resulting in a less complete estimate of bat activity at these sites. The construction of artificial roosts in urban areas is undoubtedly an important conservation action for preserving bats in such areas. Already constructed SSSs can also contribute to the available habitat of bats if constructed and managed properly. Current SSSs are already being used by bats, but with minor consideration of system size or disturbance, future SSS designs may be more well-suited for bats. Without altering construction designs, the management of established SSSs can potentially be more bat-friendly. Water management districts occasionally flush SSSs to free any debris that may be blocking the flow of water. In large rain events, water rushing in from the curb inlet and filling the pipe below can fill the space between the pipe and the manhole cover. In either situation, bats would be trapped under the cover and be at risk of drowning. In this way, SSSs could sometimes act as a population sink. The covariates that were the best predictors for raccoons were not the same for bats. While there were nine sites where we documented both species, these sites were not highly used by both species. Cichocki et al. ( 2021 ) shares an example of raccoons in the underground corridor of the Nietoperek reserve in Poland, where bats ( Myotis sp. , Plecotus auritus ) were present in 96% of raccoon scat. Our study has no evidence to support that raccoons prey upon bats, but raccoons were observed feeding on fish and amphibians within SSSs. As raccoons and bats were the two most common species observed in our study, future studies are needed to better understand their relationship in SSSs. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution The study conception and design were shaped by S.J., M.H., B.S., and A.I. A.I. and K.B. conducted material preparation and data collection, and M.A. and A.I. preformed the analysis. The initial manuscript draft was composed by A.I. and K.B., and all authors provided feedback on earlier versions of the manuscript. All authors have contributed their ideas and revisions to the final manuscript. Acknowledgments- The authors would like to thank the City of Gainesville, Florida Department of Transportation, City of Alachua, University of Florida Facilities Services, Celebration Pointe, and Turkey Creek for access to their respective stormwater systems. Data Availability All data and supporting the findings of this study are available via the lead author's github page (https://github.com/alanivory/SSS-Occupancy). References ArcGIS Pro (2024) ArcGIS Pro (Version 3.1.2) Bain TK, Cook D, Girman D (2017) Evaluating the effects of abiotic and biotic factors on movement through wildlife crossing tunnels during migration of the California Tiger Salamander, Ambystoma californiense. Herpetological Conserv Biology 12:192–201 Baker GM (2015) Quantifying wildlife use of cave entrances using remote camera traps. J Cave Karst Stud 77 Berland A, Herrmann DL, Hopton ME (2016) National assessment of Tree City USA participation according to geography and socioeconomic character-istics. Arboric Urban Forestry 42:120–130 Bullington M (2024) Stormwater Map for the City of Gainesville Burton AC, Neilson E, Moreira D et al (2015) REVIEW: Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. J Appl Ecol 52:675–685. https://doi.org/10.1111/1365-2664.12432 Che W, Zhao Y, Yang Z et al (2014) Integral stormwater management master plan and design in an ecological community. J Environ Sci 26:1818–1823 Cichocki J, Ważna A, Bator-Kocoł A et al (2021) Predation of invasive raccoon (Procyon lotor) on hibernating bats in the Nietoperek reserve in Poland. Mammalian Biology 101:57–62 Cramer PC, Portier KM (2001) Modeling Florida panther movements in response to human attributes of the landscape and ecological settings. Ecol Model 140:51–80 Dexter RW (1969) Banding and nesting studies of the Chimney Swift, 1944–1968. Ohio J Sci 69:193–213 Dixon MD (2012) Relationship between land cover and insectivorous bat activity in an urban landscape. Urban Ecosyst 15:683–695 Dodd CK Jr, Barichivich WJ, Smith LL (2004) Effectiveness of a barrier wall and culverts in reducing wildlife mortality on a heavily traveled highway in Florida. Biol Conserv 118:619–631 Duchamp JE, Sparks DW, Whitaker J, John O (2004) Foraging-habitat selection by bats at an urban–rural interface: comparison between a successful and a less successful species. Can J Zool 82:1157–1164 Fehlmann G, O’riain MJ, FÜrtbauer I, King AJ (2021) Behavioral causes, ecological consequences, and management challenges associated with wildlife foraging in human-modified landscapes. Bioscience 71:40–54 Feigley HP (1992) The ecology of the raccoon in suburban Long Island, New York and its relation to soil contamination with Baylisascaris procyonis ova. State University of New York College of Environmental Science and Forestry Ferrara FJ, Leberg PL (2005) Characteristics of positions selected by day-roosting bats under bridges in Louisiana. J Mammal 86:729–735 Fiske I, Chandler R (2011) unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance. J Stat Soft 43:1–23. https://doi.org/10.18637/jss.v043.i10 Fitzgerald TM, van Stam E, Nocera JJ, Badzinski DS (2014) Loss of nesting sites is not a primary factor limiting northern Chimney Swift populations. Popul Ecol 56:507–512 Forman RT (2003) Road ecology: science and solutions. Island Gehrt SD, Chelsvig JE (2008) Bat activity in an urban landscape: patterns at the landscape and microhabitat scale. Urban ecology: An international perspective on the interaction between humans and nature 437–453 Glista DJ, DeVault TL, DeWoody JA (2009) A review of mitigation measures for reducing wildlife mortality on roadways. Landsc urban Plann 91:1–7 Goehring HH (1957) A six year study of big brown bat survival. J Minn Acad Sci 25:222–224 Gore JA, Studenroth KR Jr (2005) Status and management of bats roosting in bridges in Florida Graser IIIWH, Gehrt SD, Hungerford LL, Anchor C (2012) Variation in demographic patterns and population structure of raccoons across an urban landscape. J Wildl Manag 76:976–986 Griffin AS, Netto K, Peneaux C (2017) Neophilia, innovation and learning in an urbanized world: a critical evaluation of mixed findings. Curr Opin Behav Sci 16:15–22. https://doi.org/10.1016/j.cobeha.2017.01.004 Hoffmann CO, Gottschang JL (1977) Numbers, distribution, and movements of a raccoon population in a suburban residential community. J Mammal 58:623–636 Hunt A, Dickens H, Whelan R (1987) Movement of mammals through tunnels under railway lines. Australian Zoologist 24:89–93 Ivory AA, Hallett MT, Scheffers B, Johnson SA (2024) Vertebrate Diversity in Stormwater Sewer Systems of Alachua County, Florida. Urban Naturalist 75:1–20 Jackson ND, Fahrig L (2011) Relative effects of road mortality and decreased connectivity on population genetic diversity. Biol Conserv 144:3143–3148 Jackson SD, Griffin CR (2000) A strategy for mitigating highway impacts on wildlife. Wildlife and highways: seeking solutions to an ecological and socio-economic dilemma. Wildl Soc 143–159 Jokimäki J, Kaisanlahti-Jokimäki M-L, Suhonen J et al (2011) Merging wildlife community ecology with animal behavioral ecology for a better urban landscape planning. Landsc Urban Plann 100:383–385. https://doi.org/10.1016/j.landurbplan.2011.02.001 Keeley B, Tuttle M (1999) Bats in American bridges Kellner KF, Smith AD, Royle JA et al (2023) The unmarked R package: Twelve years of advances in occurrence and abundance modelling in ecology. Methods Ecol Evol 14:1408–1415 Kerth G, Melber M (2009) Species-specific barrier effects of a motorway on the habitat use of two threatened forest-living bat species. Biol Conserv 142:270–279 Kettel EF, Gentle LK, Yarnell RW, Quinn JL (2019) Breeding performance of an apex predator, the peregrine falcon, across urban and rural landscapes. Urban Ecosyst 22:117–125 Le Viol I, Mocq J, Julliard R, Kerbiriou C (2009) The contribution of motorway stormwater retention ponds to the biodiversity of aquatic macroinvertebrates. Biol Conserv 142:3163–3171. https://doi.org/10.1016/j.biocon.2009.08.018 Lehrer EW, Gallo T, Fidino M et al (2021) Urban bat occupancy is highly influenced by noise and the location of water: Considerations for nature-based urban planning. Landsc Urban Plann 210:104063. https://doi.org/10.1016/j.landurbplan.2021.104063 Leivers SJ, Meierhofer MB, Pierce BL et al (2019) External temperature and distance from nearest entrance influence microclimates of cave and culvert-roosting tri‐colored bats (Perimyotis subflavus). Ecol Evol 9:14042–14052 MacKenzie DI, Nichols JD, Lachman GB et al (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248–2255 McClearn D (1992) Locomotion, posture, and feeding behavior of kinkajous, coatis, and raccoons. J Mammal 73:245–261 McCleery RA, Zweig CL, Desa MA et al (2014) A novel method for camera-trapping small mammals. Wildl Soc Bull 38:887–891 McKercher LJ, Kimball ME, Scaroni AE et al (2024) Stormwater ponds serve as variable quality habitat for diverse taxa. Wetlands Ecol Manage 32:109–131 Messmer TA (2009) Human–wildlife conflicts: emerging challenges and opportunities. Human-Wildlife Conflicts 3:10–17 Nickel BA, Suraci JP, Allen ML, Wilmers CC (2020) Human presence and human footprint have non-equivalent effects on wildlife spatiotemporal habitat use. Biol Conserv 241:108383 Nixon CM, Sullivan JB, Esker TL et al (2001) Den use by raccoons in Westcentral Illinois. Trans Ill State Acad Sci 94:59765 Peterson G (2002) Estimating Resilience Across Landscapes. Ecology and Society; Vol 6, No 1 (2002) Philcox C, Grogan A, Macdonald D (1999) Patterns of otter Lutra lutra road mortality in Britain. J Appl Ecol 748–762 Posit team (2024) RStudio: Integrated Development Environment for R Prange S, Gehrt SD, Wiggers EP (2004) Influences of anthropogenic resources on raccoon (Procyon lotor) movements and spatial distribution. J Mammal 85:483–490 Prange S, Gehrt SD, Wiggers EP (2003) Demographic factors contributing to high raccoon densities in urban landscapes. J Wildl Manag 324–333 R Core Team (2024) _R: A Language and Environment for Statistical Computing_ Rooney B, Kays R, Cove MV et al (2025) Glob Ecol Biogeogr 34:e13941. https://doi.org/10.1111/geb.13941 . SNAPSHOT USA 2019–2023: The First Five Years of Data From a Coordinated Camera Trap Survey of the United States Rydell J (1992) Exploitation of insects around streetlamps by bats in Sweden. Funct Ecol 744–750 Sacchi R, Gentilli A, Razzetti E, Barbieri F (2002) Effects of building features on density and flock distribution of feral pigeons Columba livia var. domestica in an urban environment. Can J Zool 80:48–54 Saracco JF, Siegel RB, Wilkerson RL (2011) Occupancy modeling of Black-backed Woodpeckers on burned Sierra Nevada forests. Ecosphere 2:art31. https://doi.org/10.1890/ES10-00132.1 Sawaya MA, Kalinowski ST, Clevenger AP (2014) Genetic connectivity for two bear species at wildlife crossing structures in Banff National Park. Proceedings of the Royal Society B: Biological Sciences 281:20131705 Shilling F, Collins A, Louderback-Valenzuela A et al (2018) Wildlife-Crossing mitigation effectiveness with traffic noise and light Shochat E, Lerman S, Fernández-Juricic E (2010) Birds in urban ecosystems: population dynamics, community structure, biodiversity, and conservation. Urban Ecosyst Ecol 55:75–86 Slate D (1985) Movement, activity and home range patterns among members of a high density suburban raccoon population. Rutgers University Tran HD (2016) Markov-Based Reliability Assessment for Hydraulic Design of Concrete Stormwater Pipes. J Hydraul Eng 142:06016005. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001130 U.S. Geological Survey (USGS) (2024) Annual NLCD Collection 1 Science Products. U.S. Geological Survey data release Webb EN, Ober HK, Braun de Torrez E et al (2021) Urban roosts: use of buildings by Florida bonneted bats. Urban Naturalist 42:1–11 Wheeler HE (2013) Foraging patterns of breeding Chimney Swifts (Chaetura pelagica) in relation to urban landscape features. Master’s thesis Trent University, Peterborough, Ontario, Canada Zeitler EF, Lashley MA, Blanc A et al (2023) Remote cameras capture dung burial by burrowing cricket. Food Webs 36:e00301 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 May, 2025 Read the published version in Urban Ecosystems → Version 1 posted Editorial decision: Revision requested 12 May, 2025 Reviews received at journal 06 May, 2025 Reviews received at journal 22 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviews received at journal 11 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 09 Apr, 2025 Editor assigned by journal 02 Apr, 2025 Submission checks completed at journal 02 Apr, 2025 First submitted to journal 01 Apr, 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-6355575","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440845459,"identity":"3c9183eb-dad1-43d9-906e-3e4100751b32","order_by":0,"name":"Alan A. Ivory II","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie3PMUsDMRTA8RyBy5La9Q7R+hHeUVCEw8+SULi1uJQbDw7S5T6Ag36HTq6mvMEltKvQ5VycFAqCFuxggtat8UbB/CHwAu9HCCGh0F+M2SPc0K8ErMucsKm9cB+hO5Lo9vLKFIRjF/KVbF97Cn8n/Zo+tY8qH4NGOeNmKRtKovZF7ScJxmcgVXE+QzWCpFw5QrMbDyFIThNhEECbAsCs5B0l8WHPQwbI3r7J+H0j1cK9wj58BJDbV0pHhIC50o7E1Ecy5BNLCkgftMgqMxo2NKrT68V+cnw/vU03kMPBshLDbXlx1LB6vn6eeL6/60T/jFHVYd826LgXCoVC/7BP+DRXTK4/E7EAAAAASUVORK5CYII=","orcid":"","institution":"University of Florida","correspondingAuthor":true,"prefix":"","firstName":"Alan","middleName":"A. Ivory","lastName":"II","suffix":""},{"id":440845462,"identity":"ba7cb00b-8cc8-449a-8377-0f83f4e1cecd","order_by":1,"name":"Matt T. Hallett","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Matt","middleName":"T.","lastName":"Hallett","suffix":""},{"id":440845463,"identity":"c940fde3-7beb-4816-8602-803bb3e72141","order_by":2,"name":"Miguel A. Acevedo","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"A.","lastName":"Acevedo","suffix":""},{"id":440845464,"identity":"fdfa27a9-9e62-4b80-adfb-6815c85f43c8","order_by":3,"name":"Brett R. Scheffers","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Brett","middleName":"R.","lastName":"Scheffers","suffix":""},{"id":440845465,"identity":"278fa0e8-e034-4fc2-9090-12d17fe34af7","order_by":4,"name":"Steve A. Johnson","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Steve","middleName":"A.","lastName":"Johnson","suffix":""},{"id":440845466,"identity":"7c8ce273-8426-4d2b-98f5-73e4ed2825b2","order_by":5,"name":"Kody M. Brock","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Kody","middleName":"M.","lastName":"Brock","suffix":""}],"badges":[],"createdAt":"2025-04-01 19:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6355575/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6355575/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11252-025-01748-w","type":"published","date":"2025-05-29T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80573054,"identity":"f663349c-e7c8-40de-a85d-bc56bc62529c","added_by":"auto","created_at":"2025-04-14 20:27:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115318,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of camera sites across Alachua County, Florida, USA\u003c/p\u003e","description":"","filename":"Figure1SSSMap.png","url":"https://assets-eu.researchsquare.com/files/rs-6355575/v1/b5fc00e362aaefbaee5e9921.png"},{"id":80573055,"identity":"e082e51f-32b8-4d4b-b30f-2facdfe4e704","added_by":"auto","created_at":"2025-04-14 20:27:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":373339,"visible":true,"origin":"","legend":"\u003cp\u003eHistograms of the nine site-specific covariates. (a) Impervious surface percentage within 100 m of the camera site. (b) Nearest exit from SSS from the camera site. (c) Pipe diameter of SSS. (d) Summed pipe length within 100m of the camera site. (e) System size, or length of pipe in connected SSS. (f) Distance from the camera to the bottom of the SSS pipe. (g) Number of pipes intersecting at the camera site. (h) Does the connected SSS pipe cross a road. (i) Number of lanes of traffic on the associated roadway. The red line in all histograms indicates the mean value\u003c/p\u003e","description":"","filename":"Figure2Hist.png","url":"https://assets-eu.researchsquare.com/files/rs-6355575/v1/372136aa5ab68bec873d1422.png"},{"id":80573679,"identity":"32e81c29-eacf-48ec-823f-7a50f9abc4e8","added_by":"auto","created_at":"2025-04-14 20:43:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":874917,"visible":true,"origin":"","legend":"\u003cp\u003eExample of SSS pipe being clipped to 100 m buffer around the camera site. The SSS pipes extend past the buffer region, but only the pipe length that falls within the buffer is summed toward the site's pipe availability metric\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6355575/v1/80b77b5fa9b0e2d4c3519af6.png"},{"id":80573060,"identity":"d286a1ba-56c2-4cc4-99f8-3cf6a5cbd94b","added_by":"auto","created_at":"2025-04-14 20:27:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":360123,"visible":true,"origin":"","legend":"\u003cp\u003eThe top two SSOMs used in predicting raccoon occupancy in SSSs. Covariates on the x-axis are scaled and centered. A) The overall best fitting model of p(Depth) ψ(Distance.to.Exit) to predict occupancy. B) Model p(Depth) ψ(Distance.to.Exit+Nearest.Road.Size)\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6355575/v1/bb76c908e55fab24ddbb9ea4.png"},{"id":80573063,"identity":"2d159247-83cf-41d8-a930-97d8f8c40b81","added_by":"auto","created_at":"2025-04-14 20:27:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":400504,"visible":true,"origin":"","legend":"\u003cp\u003eThe top five SSOMs as predictors of bat occupancy. Covariates on the x-axis are scaled and centered. a) Model p(.) ψ(Impervious.Percentage) as a predictor of occupancy. b) Model p(.) ψ(System.Size+Distance.to.Exit) as a predictor of occupancy. c) Model p(.) ψ(Impervious.Percentage+System.Size) as a predictor of occupancy. d) Model p(.) ψ(Impervious.Percentage+Pipe.Size) as a predictor of occupancy. e) Model p(.) ψ(System.Size) as a predictor of occupancy\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6355575/v1/3ec0e4401a2ae67766f1b141.png"},{"id":83782766,"identity":"fcf47d11-10d2-49a0-bd15-ebb232fa2b58","added_by":"auto","created_at":"2025-06-02 16:03:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2705258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6355575/v1/d55e70a7-4792-4102-a130-0542eec57eed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIdentifying the Factors That Influence Raccoon (Procyon Lotor) and Southeastern Myotis (Myotis Austroriparius) Use of Stormwater Sewer Systems\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rapid urbanization and expansion of human activities have dramatically transformed landscapes across the globe (Cramer and Portier \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Peterson \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Urbanization brings a suite of challenges for wildlife, including habitat fragmentation, altered food sources, and increased noise and light pollution (Messmer \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Shilling et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fehlmann et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Yet some species have shown resilience and adaptation to these anthropogenic changes (Shochat et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fitzgerald et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A diverse array of vertebrates, from birds to mammals, have managed to exploit urban niches, often capitalizing on the resources inadvertently provided by human activities (Jokim\u0026auml;ki et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Griffin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Birds provide some well-known examples, as peregrine falcons (\u003cem\u003eFalco peregrinus\u003c/em\u003e) make their nests on rooftops, feral pigeons (\u003cem\u003eColumba livia\u003c/em\u003e) who not only nest on rooftops but also feed on discarded human food (Sacchi et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kettel et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), chimney swifts (\u003cem\u003eChaetura pelagica\u003c/em\u003e) modify their nesting behavior to exploit man-made structures using chimneys, air shafts, and wells for roosting (Dexter \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1969\u003c/span\u003e; Wheeler \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Mammals in urban environments are less common, but raccoons (\u003cem\u003eProcyon lotor\u003c/em\u003e) are known to have smaller home ranges in urbanized areas (Slate \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Feigley \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Prange et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) due to increased stability of available resources (Hoffmann and Gottschang \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). These instances represent the plasticity of behavior that some species possess, allowing them to overcome the challenges of urban life.\u003c/p\u003e \u003cp\u003eRaccoons are an icon among urban wildlife due to their widespread distribution and frequent close association with humans (Hoffmann and Gottschang \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; McClearn \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Prange et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Graser III et al. 2012). They often forage in trash bins, on discarded human food, and can capitalize on human structures for shelter (Nixon et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Similarly, bats, such as big brown bats (\u003cem\u003eEptesicus fuscus\u003c/em\u003e) and Rafinesque's big-eared bats (\u003cem\u003eCorynorhinus rafinesquii\u003c/em\u003e), have carved out a unique niche in urban ecosystems by utilizing buildings as roosting sites and benefiting from the abundance of insects attracted to urban lights (Rydell \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Duchamp et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ferrara and Leberg \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Gehrt and Chelsvig \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These examples highlight the flexible behaviors and ecological preferences that enable these creatures to bridge the gap between their natural habitats and the anthropogenic urban landscape.\u003c/p\u003e \u003cp\u003eTo better navigate human-modified environments, some vertebrates have taken advantage of designed eco-tunnels and wildlife crossings (Jackson and Griffin \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Forman \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Glista et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Typically, these wildlife crossings are straight culverts, with the purpose of facilitating animal movements from one side of a road to the other (Philcox et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Bain et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Such construction often decreases roadkill and reduces genetic loss due to mortality (Dodd Jr et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Jackson and Fahrig \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sawaya et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStormwater sewer systems (SSSs) in the U.S. are structurally similar to the urban passages that are specifically designed for animals to use (Hunt et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Tran \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bain et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These systems, designed to manage excess rainwater and prevent flooding (Che et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), inadvertently create a network of dark, concealed spaces that may be enticing for animals seeking refuge from urban pressures. Factors such as traffic, noise, and tunnel dimensions are known to influence an animal\u0026rsquo;s likelihood of using constructed wildlife crossings (Jackson and Griffin \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Kerth and Melber \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Nickel et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and potentially similar factors may influence their probability of using SSSs.\u003c/p\u003e \u003cp\u003eWe explored the factors influencing raccoon and southeastern myotis (\u003cem\u003eMyotis austroriparius\u003c/em\u003e) occupancy within the SSSs of north Florida's urban landscape. We used single-species occupancy models (SSOMs), to analyze the relationships among construction-based factors and the presence of these species. To accomplish this goal, we deployed camera traps at numerous locations across Alachua, Co. Florida, USA, and recorded site-specific factors through field sampling and remote sensing. Our findings offer insights into the broader mechanisms by which wildlife adapts to human-altered environments. Additionally, our study can inform the design and maintenance of stormwater systems, considering the potential impacts on the wildlife communities that have become entwined with them.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eWe conducted the study in Alachua County, Florida, USA, across a range of environmental and construction conditions. Alachua County participates in the Tree City USA program (Berland et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), signifying its status as a developed area with abundant green spaces. To capture diverse conditions, our study area included urban regions, such as downtown Gainesville and the University of Florida campus, along with state highways, and less-developed areas, including neighborhoods, golf courses, and green spaces.\u003c/p\u003e \u003cp\u003eUsing underground utility and SSS shapefiles provided by the City of Gainesville and the University of Florida Facility Services (UFFS), we selected study sites based on the following criteria: (1) SSS pipes were at least 30 cm in diameter to allow for a camera trap to function properly (McCleery et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and animal access, (2) located in areas approved by the City of Gainesville, Florida Department of Transportation, or the UFFS, (3) exclusion of locations with damaged or broken pipes, and (4) only one sampling site was used per SSS to maintain sampling independence. At the study\u0026rsquo;s conclusion, we removed an additional site that lacked sufficient trap-nights to construct a robust occupancy model. This left 33 camera sites in unique \u0026ldquo;sewersheds\u0026rdquo; across Alachua County (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), comprising 12 simple culverts and 21 more complex systems. For the southeastern myotis analysis, we included data from all 33 cameras, while for the raccoon analysis we removed two culvert sites due to water inundation throughout the study and, in one case, a metal barricade at the pipe outlet, which restricted ground entry.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCamera-Trap Surveys\u003c/h3\u003e\n\u003cp\u003eWe installed camera-traps (Bushell Care S-4K No Glow #119949C; Prime Combo Low Glow #119932CB; Bushnell\u0026reg;, KS, USA; Moultrie M-880 #MCG12594 Moultrie\u0026reg;, AL, USA) at each of the 33 sites from February through June 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Where possible, we mounted cameras using a magnetic attachment to the back, providing a parallel view of the bottom of the SSS pipe (Ivory et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In non-metal SSSs, we attached cameras to a tree at the nearest entrance of the SSS pipe. Due to variations in SSS construction, mounting distances were not fully standardized, but, whenever feasible, cameras were placed about 1 m from the pipe (McCleery et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Baker \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zeitler et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), corresponding to the average depth of SSSs in our study area.\u003c/p\u003e \u003cp\u003eWe collected 10-second videos for each motion response to improve detection probability, given the close proximity of wildlife to the camera. This setup enabled us to identify most vertebrates to species level, even without a complete view of each individual. Camera-traps were active for a total of 1,921 trap-nights, averaging 58.2 trap nights per site. Variations in trap nights per site were due to water damage affecting five cameras and the theft of two cameras. We conducted biweekly visits to all cameras to download video data and replaced batteries whenever the battery indicator dropped to one bar.\u003c/p\u003e\n\u003ch3\u003eSite-Specific Factors\u003c/h3\u003e\n\u003cp\u003eAs our study is one of the first to determine the drivers of wildlife use of SSSs, at each of the 33 camera sites we measured a broad array of site-specific environmental and construction-based variables that may drive raccoon and bat occupancy within these systems. One-time measurements included the distance from a camera to the nearest exit via a pipe within its sewershed (we did not consider curb inlets as exits), distance from camera to ground (depth), length of the connected stormwater system, impervious surface percentage as a proxy for human disturbance above the system, SSS pipe diameter, number of pipe nodes, pipe availability within 100 m, the number of lanes of traffic of the associated road nearest the camera, and if the SSS crossed under a road (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We measured the distance to the nearest wetland, but this factor was removed from the analysis due to a strong correlation with the distance to the nearest exit (r\u0026thinsp;=\u0026thinsp;0.86). We also collected information related to the speed limit of the associated road, but this variable was strongly correlated to the nearest road size variable (r\u0026thinsp;=\u0026thinsp;0.80), so we also removed this variable from the analysis.\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\u003eList of covariates used for SSOM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable Type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem.Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength (m) of pipes in the system with a camera trap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to Exit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance (m) from the camera to the end of the associated SSS pipe.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePipe.within.100M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength (m) of connected SSS pipe within 100M of the camera trap.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePipe.Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePipe Diameter (cm.) of associated SSS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of pipes intersecting at the camera site.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInteger\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCross.Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoes the connected SSS pipe cross a road?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (Yes/No)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNearest.Road.Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of lanes of traffic on the associated roadway.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInteger\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from camera mount to ground/bottom of SSS pipe.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpervious.Percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of 100-meter buffer covered by an impervious surface.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\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\u003eWe used the National Land Cover Database (NLCD) 2023 CONUS Fractional Impervious Surface dataset to produce the average percentage of impervious surfaces within 100 meters of each sampling site (Dixon \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; U.S. Geological Survey (USGS) \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The NLCD Fractional Impervious Surface dataset provides the percentage of ground cover that is classified as impervious, such as pavement and buildings, at the 30-meter resolution. Impervious surface values that fall within a 100 m buffer of each sampling site were averaged. The percentage of the camera sites\u0026rsquo; 100 m buffers that were covered by an impervious surface ranged from 4\u0026ndash;64%, with an average of 33% of the cameras\u0026rsquo; buffer consisting of impervious surfaces (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). As SSSs are associated with urbanized areas, more complex stormwater systems tended to have a higher average impervious surface percentage (35%) compared to culverts (30%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe measured the distance from a camera to the nearest sewer pipe exit by using the measure distance tool in ArcGIS Pro to trace the shortest route to the desired point (ArcGIS Pro \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For the distance to the nearest exit measurement, we defined an exit as a pipe outlet, thus excluding curb inlets and grates that varied in size and often precluded animal access. Distances to exits from a camera site varied from 0 to 225 m, with an average of 43 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). A total of nine cameras were categorized as having a 0 m distance to exit. This is because the camera was attached to a tree at the pipe opening or was attached to the opening of the pipe with a magnetic mount. For this reason, seven of the culvert camera sites had a distance to exit of 0 m, with the average distance in culvert SSSs being 8 m. Cameras in complex systems tended to be further from an exit with an average distance of 67 m.\u003c/p\u003e \u003cp\u003eThe stormwater pipe diameter and the number of pipe nodes at the camera site were derived from information in the attribute table on manhole nodes of stormwater shapefiles from the City of Gainesville and UFFS databases (Bullington \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The pipe diameter of SSSs used in our study ranged from 30 to 183 cm, with a mean of 74 cm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Complex systems averaged a pipe diameter of 61 cm, while culverts were on average 96 cm.\u003c/p\u003e \u003cp\u003eTo measure the length of pipes within 100 m of the camera site, we clipped the SSS pipe\u0026rsquo;s line feature to the camera sites\u0026rsquo; 100-meter buffer, where the pipe length that falls within the buffer is summed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Of the camera sites that we used, there was an average of 180 m of pipe available within 100 m of the camera site. Pipe availability ranged from 14 to 537 m. As culverts, by definition, were limited in size, the length of pipe within 100 m of a camera site was less, with an average of 75 m, compared to 240 m for complex systems.\u003c/p\u003e \u003cp\u003eWe used the Measure Distance tool in ArcGIS Pro to determine the size of a sewershed by tracing the connected SSS pipes associated with the camera site. The length of pipes connected to camera sites ranged from 12 to 6,913 m, with an average summed length of 879 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Culverts were shorter with an average connected pipe length of 29 m compared to more complex sewersheds with an average system size of 1,366 m.\u003c/p\u003e \u003cp\u003eDuring camera-trap deployment, we measured the distance from the camera to the floor of the pipe within the SSS. To collect this measurement to the nearest cm, we used a tape to measure from the rim of the manhole opening to the pipe floor. The average distance from the camera to the base of the pipe was 1.56 m and ranged from 0.40 to 4.90 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Many culverts were constructed of concrete, preventing the use of magnetic mounts, leading to the average distance from the camera to the pipe being further than cameras in complex systems. The average distance for culverts was 1.93 m, whereas the cameras in complex SSSs had an average distance to the floor of the pipe of 1.36 m.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe number of intersecting pipes at a camera site ranged from 1\u0026ndash;4, with an average of 1.9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). A value of 1 indicates that the camera was positioned either at a pipe opening or on a catch basin with only one inlet. This setup allows water to enter from a grate or curb inlet above, but it involves only one drainage pipe. An average value of near 2 indicates that most often the camera trap was at a site with a pipe extending in both directions. As systems are defined as being more complex than culverts, the average number of nodes at these camera sites was 2.3, whereas culverts had an average of 1.3 nodes.\u003c/p\u003e \u003cp\u003eNearly all sewersheds containing a camera crossed under a road (82%; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). This was slightly higher in complex systems (86%), compared to culverts (75%). While culverts are typically designed to move water from one side of the road to the other, our study included one culvert that crossed under a grass walking trail, another culvert that connected two ponds, and a third culvert crossed under a bike path. We also recorded the number of lanes of traffic on the associated roadway. The SSSs we used in our study had an average of 2.1 lanes of traffic (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei). Complex systems averaged slightly smaller road size with 2 lanes of traffic, compared to 2.3 lanes for culverts.\u003c/p\u003e\n\u003ch3\u003eAnalysis\u003c/h3\u003e\n\u003cp\u003eWe used the wildlife observations from our camera-traps and the site-specific construction factors to construct single-species occupancy models (SSOMs; MacKenzie et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Although we detected a total of 35 species within the SSSs (Ivory et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), we only constructed occupancy models for raccoons and southeastern myotis. These species were chosen as they had the most widespread presence in SSSs, and for the southeastern myotis, their conservation interest as a result to their sensitivity to urbanization and human disturbance (Duchamp et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Gehrt and Chelsvig \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Webb et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lehrer et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe constructed SSOMs in R package \u003cem\u003eunmarked\u003c/em\u003e, as we are using camera-trap data of unmarked wildlife to track occupancy (Fiske and Chandler \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Burton et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kellner et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Posit team \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; R Core Team \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We recorded each wildlife observation individually. As a first step, we grouped species occurrence counts per week by site number, where occupancy for each species was denoted as a one or a zero for each week. To ensure all sites were given equal effort, we used an effort matrix to outline which weeks a site had an active camera for all seven days. Sites with a camera active for less than seven days most often occurred due to dead camera batteries, vegetation overgrowth, or humans tampering with the camera. If a site did not have an active camera for the full week we eliminated that week from the effort matrix, and in turn, the associated observation count data from that week were not used in the occupancy model. We scaled and centered the site-specific covariates to aid the \u003cem\u003eunmarked\u003c/em\u003e package in parameter estimation (Saracco et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe occupancy models with covariates were compared to a \u0026ldquo;null\u0026rdquo; (intercept-only) model, with no predictors for detection (p) or occupancy (ψ). Models with covariates included those with no predictor for detection and site-specific covariates as predictors for occupancy. Models with covariates also included models with the distance from the camera to the ground (\u0026ldquo;Depth\u0026rdquo;) as a predictor for detection with site-specific covariates as predictors for occupancy (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Covariates were used as predictors for occupancy individually and additively.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe observed raccoons most often, with a total of 1,755 detections. Southeastern myotis were observed on 418 occasions. Of the 33 (31 for raccoons) camera sites that we used for occupancy models, raccoons were recorded at 21 sites, while southeastern myotis were detected at 15 sites. The number of observations per site exhibited considerable variability for both species. Raccoon observations per site for the extent of our study ranged from 2 to 517 sightings, while bat observations ranged from 1 to 300. Raccoons were detected by most cameras with similar frequencies. Conversely, bat observations were primarily concentrated at three sites, with eight sites each recording only a single observation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRaccoons\u003c/h2\u003e \u003cp\u003eThe most parsimonious model explaining the occupancy of raccoons included distance to exit as a covariate for occupancy and depth as a covariate for the probability of detection (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The second best model ( ΔAIC\u0026thinsp;=\u0026thinsp;2.07) also included depth as a covariate for the probability of detection, but with both the distance to exit and the nearest road size as predictors of occupancy. Notably, the top five models all included distance to exit as a covariate for occupancy, and the depth of the sewer pipe from the camera was included as a covariate for detection in the top 18 models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eAIC table for raccoon SSOMs across all SSSs. p is the probability of detection and ψ is the probability of occupancy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Distance.to.Exit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Distance.to.Exit\u0026thinsp;+\u0026thinsp;Nearest.Road.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Distance.to.Exit\u0026thinsp;+\u0026thinsp;Pipe.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Distance.to.Exit\u0026thinsp;+\u0026thinsp;System.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Distance.to.Exit\u0026thinsp;+\u0026thinsp;Nodes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Cross.Road)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Impervious.Percentage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Pipe.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Impervious.Percentage\u0026thinsp;+\u0026thinsp;Pipe.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(System.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Cross.Road\u0026thinsp;+\u0026thinsp;Nearest.Road.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Nearest.Road.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Impervious.Percentage\u0026thinsp;+\u0026thinsp;System.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Nodes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Pipes.within.100M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Depth)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(System.Size\u0026thinsp;+\u0026thinsp;Depth)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Pipes.within.100M\u0026thinsp;+\u0026thinsp;Nodes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(.) ψ(Distance.to.Exit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(.) ψ(.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\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\u003eIn the best model that included the distance to exit as a covariate for occupancy and depth as a predictor of detection, the probability of detection increased 2.01 (\u0026plusmn;\u0026thinsp;0.42 SE) times with a unit increase in Depth (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, in the top model, the probability of occupancy decreased 0.29 (\u0026plusmn;\u0026thinsp;0.15 SE) times with a unit increase in the distance to an exit (p\u0026thinsp;=\u0026thinsp;0.016; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In the second-best model, which included the distance to exit and the nearest road size as a predictor of occupancy, with depth as a predictor of detection, the probability of detection continued to increase 2.01 (\u0026plusmn;\u0026thinsp;0.42 SE) times with a unit increase in Depth (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The probability of occupancy continued to decrease 0.29 (\u0026plusmn;\u0026thinsp;0.15 SE) times with a unit increase in the distance to exit (p\u0026thinsp;=\u0026thinsp;0.019), while the odds of occupancy decreased 0.65 (\u0026plusmn;\u0026thinsp;0.32 SE) times with a unit increase in the nearest road size (p\u0026thinsp;=\u0026thinsp;0.377). Based on these results, we would expect less raccoon occupancy further into a system near large roads, although the relationship between road size and raccoon occupancy has greater uncertainty (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBats\u003c/h3\u003e\n\u003cp\u003eFor bat occupancy models constructed using SSOMs, seven models performed better than the null, with five models within two ΔAIC units (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The top five models did not include any predictors of detection, while three of the top five models did include impervious surface percentage as a detector of occupancy. The top performing model included the sites impervious surface percentage as the sole predictor of occupancy, while the second-best model included the system size and the distance to exit covariates as predictors of occupancy (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The remaining three models with a ΔAIC within two units of the top model includes a model with the impervious surface percentage and the system size as predictors of occupancy, the impervious surface percentage and the pipe size as predictors of occupancy, and a model with the system size as a predictor of occupancy.\u003c/p\u003e \u003cp\u003eThe best model, using the impervious surface percentage as a predictor of bat occupancy, predicts the odds of bats occupying a SSS decreased 0.39 (\u0026plusmn;\u0026thinsp;0.17 SE) times for a unit increase in the impervious surface percentage (p\u0026thinsp;=\u0026thinsp;0.036; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The second-best model, including the system size and the distance to exit from a sampling location, suggests that bat occupancy increased 2.79 (\u0026plusmn;\u0026thinsp;2.09 SE) times for a unit increase in the distance to exit (p\u0026thinsp;=\u0026thinsp;0.171), but the odds of occupying a SSS decreased by 0.13 (\u0026plusmn;\u0026thinsp;0.28 SE) times for a unit increase in the system size (p\u0026thinsp;=\u0026thinsp;0.341; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAIC table for bat SSOMs across all SSSs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(.) ψ(Impervious.Percentage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(.) ψ(System.Size\u0026thinsp;+\u0026thinsp;Distance.to.Exit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(.) ψ(Impervious.Percentage\u0026thinsp;+\u0026thinsp;System.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(.) ψ(Impervious.Percentage\u0026thinsp;+\u0026thinsp;Pipe.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(.) ψ(System.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Impervious.Percentage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(Depth) ψ(Distance.to.Exit\u0026thinsp;+\u0026thinsp;System.Size)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep(.)ψ(.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\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\u003eAn additional two models within two ΔAIC of the top model that contained the impervious percentage covariant suggested that the odds of bats occupancy in a SSS decreased by 0.46 (\u0026plusmn;\u0026thinsp;0.21 SE; p\u0026thinsp;=\u0026thinsp;0.090) and 0.41 (\u0026plusmn;\u0026thinsp;0.18 SE; p\u0026thinsp;=\u0026thinsp;0.043) times, respectively, for a unit increase in the impervious surface percentage above ground. Similar to the second-best model, the model with the impervious surface percentage and the system size, system size continues to be negatively related to bat occupancy, suggesting that bat occupancy decreased 0.49 (\u0026plusmn;\u0026thinsp;0.37 SE) times per unit increase in the system size (p\u0026thinsp;=\u0026thinsp;0.346; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). In the model containing the impervious surface percentage and the pipe size as predictors of occupancy, bat occupancy was predicted to have increased 1.52 (\u0026plusmn;\u0026thinsp;0.72 SE) times for a unit increase in pipe size (p\u0026thinsp;=\u0026thinsp;0.377). According to this model, bats are more likely to occupy larger piped SSSs with lower levels of above-ground impervious surfaces (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). The final model with a ΔAIC less than two was a model with no predictors for detection and the system size as the sole predictor of occupancy, which suggested that bat occupancy decreased 0.35 (\u0026plusmn;\u0026thinsp;0.30 SE) times for a unit increase in the system size (p\u0026thinsp;=\u0026thinsp;0.224; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). Note that impervious surface had a significant relationship (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with bat occupancy in the best model, and in the model containing the impervious surface and the pipe size, while it did not have a significant relationship in the model containing the impervious surface percentage and the system size. No other covariates had a significant relationship with bat occupancy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRaccoons\u003c/h2\u003e \u003cp\u003eOur results show that the probability of raccoon occupancy decreased with an increase in the distance to the nearest exit. While the distance to the nearest exit covariate was included in the top five models, all models with additional covariates performed worse than the model where the distance to an exit was the sole predictor of occupancy. While raccoon occupancy decreased as the distance from an exit in the SSS increased, raccoons are still predicted to travel in systems up to three times further than the average distance to exit of camera sites used in our study. The depth of the sewer system, or the distance between the ground and the camera, is a relevant factor to consider when considering detection of raccoons, as the probability of detection increased two times with a unit increase in the depth covariant.\u003c/p\u003e \u003cp\u003eThis importance of being near an exit for raccoons may be a response to prey availability in the form of other species that venture into the SSS or fall in from the curb inlets unwittingly. As raccoons have been occasionally observed entering and exiting a SSS using curb inlets, their selection of areas near a pipe outlet may be driven by other species that are restricted to such areas. The aquatic life that we observed raccoons feeding on are likely to remain in proximity to a pipe outlet due to the presence of associated water bodies (Le Viol et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ivory et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; McKercher et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, this proximity to water bodies may encourage raccoons to stay in these areas as well. Additionally, light available from curb inlets may be insufficient, promoting more activity near a pipe outlet.\u003c/p\u003e \u003cp\u003eOur results found that raccoon occupancy declined 0.65 times with a unit increase in the nearest road size. The number of lanes of traffic is influenced by traffic volume and determines the width of road. This in part determines the degree of fragmentation of the habitat above ground (Prange et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Along with the negative relationship raccoons may have with vehicles as it relates to a risk of roadkill, traffic noise may deter them from using a nearby SSSs (Shilling et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Of the 31 sampling sites we monitored, 23 were adjacent to a two-laned road. This may contribute to some of the uncertainty in our findings, as the distance to the nearest road covariant deviated from the average. The majority of our study took place in Gainesville, Florida, a medium-sized college town, where two-lane roads are the norm, except for major highways that pass through the city.\u003c/p\u003e \u003cp\u003eRaccoons are generalists and respond well to human disturbance (Graser III et al. 2012). The percentage of impervious surface covariant, which was used as a proxy for human disturbance, did not perform well, with a ΔAIC of 4.73 and a weight of 0.032. This suggests that the level of human disturbance was not an important factor when considering a SSS's suitability for raccoons. At the site with the most impervious surfaces (64%), 27 of the 191 raccoon observations were recorded from 12:00 PM to 5:00 PM, when foot traffic above the grate was assumed to be at its highest. For a primarily nocturnal species (Rooney et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), this level of raccoon presence with high levels of human activity is one such example of why human disturbance may not be important for raccoons considering using an SSS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBats\u003c/h2\u003e \u003cp\u003eWhile there was not a clear top model for bat occupancy, the percentage of impervious surfaces at a site was a common covariate across three of the top five models within two ΔAIC. Our results suggest that bat occupancy decreased\u0026thinsp;~\u0026thinsp;0.42 times for a unit increase in impervious surfaces. The system size covariant, also included in three of the top five occupancy models, suggested that an increase in the system size decreases the odds of bat occupancy by about 0.32 times. Bats are well documented in the U.S. roosting under bridges and culverts (Goehring \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1957\u003c/span\u003e; Keeley and Tuttle \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Leivers et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such locations are typically in areas with limited impervious surfaces, along rural roads, and would be below the average SSS size used in our study. Our findings are in line with Dixon (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), who found that insectivorous bat activity is negatively related to impervious surfaces; although impervious surfaces were not included as a predictor in the top models for \u003cem\u003eMyotis\u003c/em\u003e sp. in their study. Of the 418 bat observations we documented at sites included in the SSOMs, only 16 observations occurred at a culvert site. By comparison, smaller complex SSSs were frequently used by bats. While the role of SSSs as habitat for bats is difficult to fully capture based on camera trap footage, we observed bats both roosting on manhole covers, where a camera was attached, and flying within the sewer pipes, traveling within the SSSs and feeding on insects.\u003c/p\u003e \u003cp\u003eThe second-best model included the system size covariate additively with the distance to exit covariate as predictors of bat occupancy. Seemingly, bats prefer to be further from an exit, but not in too large of an SSS. This may in part be due to increased noise near roads. Roosting sites closer to an exit may be exposed to more noise and less preferable to bats (Kerth and Melber \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Being far from a road in a relatively small system may minimize noise disturbance and not be as large of an energy investment as using a larger SSS.\u003c/p\u003e \u003cp\u003eSeasonality may be an important unaccounted-for factor when creating these occupancy models (Goehring \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1957\u003c/span\u003e; Gore and Studenroth Jr \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). While our study included some of the colder months in northern Florida, a more complete record of potential winter hibernation is crucial. It is also important to note that at the sites where we documented the most bats, the camera would be triggered hundreds of times a day, draining batteries sooner and, in turn, resulting in a less complete estimate of bat activity at these sites.\u003c/p\u003e \u003cp\u003eThe construction of artificial roosts in urban areas is undoubtedly an important conservation action for preserving bats in such areas. Already constructed SSSs can also contribute to the available habitat of bats if constructed and managed properly. Current SSSs are already being used by bats, but with minor consideration of system size or disturbance, future SSS designs may be more well-suited for bats. Without altering construction designs, the management of established SSSs can potentially be more bat-friendly. Water management districts occasionally flush SSSs to free any debris that may be blocking the flow of water. In large rain events, water rushing in from the curb inlet and filling the pipe below can fill the space between the pipe and the manhole cover. In either situation, bats would be trapped under the cover and be at risk of drowning. In this way, SSSs could sometimes act as a population sink.\u003c/p\u003e \u003cp\u003eThe covariates that were the best predictors for raccoons were not the same for bats. While there were nine sites where we documented both species, these sites were not highly used by both species. Cichocki et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) shares an example of raccoons in the underground corridor of the Nietoperek reserve in Poland, where bats (\u003cem\u003eMyotis sp.\u003c/em\u003e, \u003cem\u003ePlecotus auritus\u003c/em\u003e) were present in 96% of raccoon scat. Our study has no evidence to support that raccoons prey upon bats, but raccoons were observed feeding on fish and amphibians within SSSs. As raccoons and bats were the two most common species observed in our study, future studies are needed to better understand their relationship in SSSs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe study conception and design were shaped by S.J., M.H., B.S., and A.I. A.I. and K.B. conducted material preparation and data collection, and M.A. and A.I. preformed the analysis. The initial manuscript draft was composed by A.I. and K.B., and all authors provided feedback on earlier versions of the manuscript. All authors have contributed their ideas and revisions to the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments-\u003c/h2\u003e \u003cp\u003eThe authors would like to thank the City of Gainesville, Florida Department of Transportation, City of Alachua, University of Florida Facilities Services, Celebration Pointe, and Turkey Creek for access to their respective stormwater systems.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data and supporting the findings of this study are available via the lead author's github page (https://github.com/alanivory/SSS-Occupancy).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArcGIS Pro (2024) ArcGIS Pro (Version 3.1.2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBain TK, Cook D, Girman D (2017) Evaluating the effects of abiotic and biotic factors on movement through wildlife crossing tunnels during migration of the California Tiger Salamander, Ambystoma californiense. Herpetological Conserv Biology 12:192\u0026ndash;201\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaker GM (2015) Quantifying wildlife use of cave entrances using remote camera traps. J Cave Karst Stud 77\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerland A, Herrmann DL, Hopton ME (2016) National assessment of Tree City USA participation according to geography and socioeconomic character-istics. Arboric Urban Forestry 42:120\u0026ndash;130\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBullington M (2024) Stormwater Map for the City of Gainesville\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurton AC, Neilson E, Moreira D et al (2015) REVIEW: Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. J Appl Ecol 52:675\u0026ndash;685. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2664.12432\u003c/span\u003e\u003cspan address=\"10.1111/1365-2664.12432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChe W, Zhao Y, Yang Z et al (2014) Integral stormwater management master plan and design in an ecological community. J Environ Sci 26:1818\u0026ndash;1823\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCichocki J, Ważna A, Bator-Kocoł A et al (2021) Predation of invasive raccoon (Procyon lotor) on hibernating bats in the Nietoperek reserve in Poland. Mammalian Biology 101:57\u0026ndash;62\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCramer PC, Portier KM (2001) Modeling Florida panther movements in response to human attributes of the landscape and ecological settings. Ecol Model 140:51\u0026ndash;80\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDexter RW (1969) Banding and nesting studies of the Chimney Swift, 1944\u0026ndash;1968. Ohio J Sci 69:193\u0026ndash;213\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDixon MD (2012) Relationship between land cover and insectivorous bat activity in an urban landscape. Urban Ecosyst 15:683\u0026ndash;695\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDodd CK Jr, Barichivich WJ, Smith LL (2004) Effectiveness of a barrier wall and culverts in reducing wildlife mortality on a heavily traveled highway in Florida. Biol Conserv 118:619\u0026ndash;631\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuchamp JE, Sparks DW, Whitaker J, John O (2004) Foraging-habitat selection by bats at an urban\u0026ndash;rural interface: comparison between a successful and a less successful species. Can J Zool 82:1157\u0026ndash;1164\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFehlmann G, O\u0026rsquo;riain MJ, F\u0026Uuml;rtbauer I, King AJ (2021) Behavioral causes, ecological consequences, and management challenges associated with wildlife foraging in human-modified landscapes. Bioscience 71:40\u0026ndash;54\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeigley HP (1992) The ecology of the raccoon in suburban Long Island, New York and its relation to soil contamination with Baylisascaris procyonis ova. State University of New York College of Environmental Science and Forestry\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerrara FJ, Leberg PL (2005) Characteristics of positions selected by day-roosting bats under bridges in Louisiana. J Mammal 86:729\u0026ndash;735\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiske I, Chandler R (2011) unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance. J Stat Soft 43:1\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v043.i10\u003c/span\u003e\u003cspan address=\"10.18637/jss.v043.i10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFitzgerald TM, van Stam E, Nocera JJ, Badzinski DS (2014) Loss of nesting sites is not a primary factor limiting northern Chimney Swift populations. Popul Ecol 56:507\u0026ndash;512\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForman RT (2003) Road ecology: science and solutions. Island\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGehrt SD, Chelsvig JE (2008) Bat activity in an urban landscape: patterns at the landscape and microhabitat scale. Urban ecology: An international perspective on the interaction between humans and nature 437\u0026ndash;453\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlista DJ, DeVault TL, DeWoody JA (2009) A review of mitigation measures for reducing wildlife mortality on roadways. Landsc urban Plann 91:1\u0026ndash;7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoehring HH (1957) A six year study of big brown bat survival. J Minn Acad Sci 25:222\u0026ndash;224\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGore JA, Studenroth KR Jr (2005) Status and management of bats roosting in bridges in Florida\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraser IIIWH, Gehrt SD, Hungerford LL, Anchor C (2012) Variation in demographic patterns and population structure of raccoons across an urban landscape. J Wildl Manag 76:976\u0026ndash;986\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffin AS, Netto K, Peneaux C (2017) Neophilia, innovation and learning in an urbanized world: a critical evaluation of mixed findings. Curr Opin Behav Sci 16:15\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cobeha.2017.01.004\u003c/span\u003e\u003cspan address=\"10.1016/j.cobeha.2017.01.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffmann CO, Gottschang JL (1977) Numbers, distribution, and movements of a raccoon population in a suburban residential community. J Mammal 58:623\u0026ndash;636\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunt A, Dickens H, Whelan R (1987) Movement of mammals through tunnels under railway lines. Australian Zoologist 24:89\u0026ndash;93\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvory AA, Hallett MT, Scheffers B, Johnson SA (2024) Vertebrate Diversity in Stormwater Sewer Systems of Alachua County, Florida. Urban Naturalist 75:1\u0026ndash;20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJackson ND, Fahrig L (2011) Relative effects of road mortality and decreased connectivity on population genetic diversity. Biol Conserv 144:3143\u0026ndash;3148\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJackson SD, Griffin CR (2000) A strategy for mitigating highway impacts on wildlife. Wildlife and highways: seeking solutions to an ecological and socio-economic dilemma. Wildl Soc 143\u0026ndash;159\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJokim\u0026auml;ki J, Kaisanlahti-Jokim\u0026auml;ki M-L, Suhonen J et al (2011) Merging wildlife community ecology with animal behavioral ecology for a better urban landscape planning. Landsc Urban Plann 100:383\u0026ndash;385. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landurbplan.2011.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.landurbplan.2011.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeeley B, Tuttle M (1999) Bats in American bridges\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKellner KF, Smith AD, Royle JA et al (2023) The unmarked R package: Twelve years of advances in occurrence and abundance modelling in ecology. Methods Ecol Evol 14:1408\u0026ndash;1415\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKerth G, Melber M (2009) Species-specific barrier effects of a motorway on the habitat use of two threatened forest-living bat species. Biol Conserv 142:270\u0026ndash;279\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKettel EF, Gentle LK, Yarnell RW, Quinn JL (2019) Breeding performance of an apex predator, the peregrine falcon, across urban and rural landscapes. Urban Ecosyst 22:117\u0026ndash;125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Viol I, Mocq J, Julliard R, Kerbiriou C (2009) The contribution of motorway stormwater retention ponds to the biodiversity of aquatic macroinvertebrates. Biol Conserv 142:3163\u0026ndash;3171. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocon.2009.08.018\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2009.08.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehrer EW, Gallo T, Fidino M et al (2021) Urban bat occupancy is highly influenced by noise and the location of water: Considerations for nature-based urban planning. Landsc Urban Plann 210:104063. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landurbplan.2021.104063\u003c/span\u003e\u003cspan address=\"10.1016/j.landurbplan.2021.104063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeivers SJ, Meierhofer MB, Pierce BL et al (2019) External temperature and distance from nearest entrance influence microclimates of cave and culvert-roosting tri‐colored bats (Perimyotis subflavus). Ecol Evol 9:14042\u0026ndash;14052\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacKenzie DI, Nichols JD, Lachman GB et al (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248\u0026ndash;2255\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcClearn D (1992) Locomotion, posture, and feeding behavior of kinkajous, coatis, and raccoons. J Mammal 73:245\u0026ndash;261\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCleery RA, Zweig CL, Desa MA et al (2014) A novel method for camera-trapping small mammals. Wildl Soc Bull 38:887\u0026ndash;891\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKercher LJ, Kimball ME, Scaroni AE et al (2024) Stormwater ponds serve as variable quality habitat for diverse taxa. Wetlands Ecol Manage 32:109\u0026ndash;131\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMessmer TA (2009) Human\u0026ndash;wildlife conflicts: emerging challenges and opportunities. Human-Wildlife Conflicts 3:10\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNickel BA, Suraci JP, Allen ML, Wilmers CC (2020) Human presence and human footprint have non-equivalent effects on wildlife spatiotemporal habitat use. Biol Conserv 241:108383\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNixon CM, Sullivan JB, Esker TL et al (2001) Den use by raccoons in Westcentral Illinois. Trans Ill State Acad Sci 94:59765\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeterson G (2002) Estimating Resilience Across Landscapes. Ecology and Society; Vol 6, No 1 (2002)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhilcox C, Grogan A, Macdonald D (1999) Patterns of otter Lutra lutra road mortality in Britain. J Appl Ecol 748\u0026ndash;762\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePosit team (2024) RStudio: Integrated Development Environment for R\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrange S, Gehrt SD, Wiggers EP (2004) Influences of anthropogenic resources on raccoon (Procyon lotor) movements and spatial distribution. J Mammal 85:483\u0026ndash;490\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrange S, Gehrt SD, Wiggers EP (2003) Demographic factors contributing to high raccoon densities in urban landscapes. J Wildl Manag 324\u0026ndash;333\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team (2024) _R: A Language and Environment for Statistical Computing_\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRooney B, Kays R, Cove MV et al (2025) Glob Ecol Biogeogr 34:e13941. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/geb.13941\u003c/span\u003e\u003cspan address=\"10.1111/geb.13941\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. SNAPSHOT USA 2019\u0026ndash;2023: The First Five Years of Data From a Coordinated Camera Trap Survey of the United States\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRydell J (1992) Exploitation of insects around streetlamps by bats in Sweden. Funct Ecol 744\u0026ndash;750\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSacchi R, Gentilli A, Razzetti E, Barbieri F (2002) Effects of building features on density and flock distribution of feral pigeons Columba livia var. domestica in an urban environment. Can J Zool 80:48\u0026ndash;54\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaracco JF, Siegel RB, Wilkerson RL (2011) Occupancy modeling of Black-backed Woodpeckers on burned Sierra Nevada forests. Ecosphere 2:art31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/ES10-00132.1\u003c/span\u003e\u003cspan address=\"10.1890/ES10-00132.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSawaya MA, Kalinowski ST, Clevenger AP (2014) Genetic connectivity for two bear species at wildlife crossing structures in Banff National Park. Proceedings of the Royal Society B: Biological Sciences 281:20131705\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShilling F, Collins A, Louderback-Valenzuela A et al (2018) Wildlife-Crossing mitigation effectiveness with traffic noise and light\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShochat E, Lerman S, Fern\u0026aacute;ndez-Juricic E (2010) Birds in urban ecosystems: population dynamics, community structure, biodiversity, and conservation. Urban Ecosyst Ecol 55:75\u0026ndash;86\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlate D (1985) Movement, activity and home range patterns among members of a high density suburban raccoon population. Rutgers University\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTran HD (2016) Markov-Based Reliability Assessment for Hydraulic Design of Concrete Stormwater Pipes. J Hydraul Eng 142:06016005. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1061/(ASCE)HY.1943-7900.0001130\u003c/span\u003e\u003cspan address=\"10.1061/(ASCE)HY.1943-7900.0001130\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Geological Survey (USGS) (2024) Annual NLCD Collection 1 Science Products. U.S. Geological Survey data release\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebb EN, Ober HK, Braun de Torrez E et al (2021) Urban roosts: use of buildings by Florida bonneted bats. Urban Naturalist 42:1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWheeler HE (2013) Foraging patterns of breeding Chimney Swifts (Chaetura pelagica) in relation to urban landscape features. Master\u0026rsquo;s thesis Trent University, Peterborough, Ontario, Canada\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeitler EF, Lashley MA, Blanc A et al (2023) Remote cameras capture dung burial by burrowing cricket. Food Webs 36:e00301\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":"urban-ecosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ueco","sideBox":"Learn more about [Urban Ecosystems](https://www.springer.com/journal/11252)","snPcode":"11252","submissionUrl":"https://submission.nature.com/new-submission/11252/3","title":"Urban Ecosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Urbanization, Stormwater management, Occupancy modeling, Corridors","lastPublishedDoi":"10.21203/rs.3.rs-6355575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6355575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWildlife living within human-dominated and/or modified landscapes may explore and use unconventional habitats. Our study investigates the overlooked potential of stormwater sewer systems (SSSs) as habitat for two urban-dwelling species: raccoons (\u003cem\u003eProcyon lotor\u003c/em\u003e) and southeastern myotis bats (\u003cem\u003eMyotis austroriparius\u003c/em\u003e). Here we focus specifically on the construction-based factors that most greatly affect the occupancy of these two species within the SSS of Alachua Co., Florida. With many vertebrates using SSSs for movement, foraging, and roosting, knowing what factors influence a system's usability is important when designing urban corridors. Our findings suggest that raccoon occupancy in SSSs was most closely related to the proximity to the nearest exit, but bats seem to select roosting sites based on a multitude of factors, including the size of the SSS, the distance to the nearest exit, and the level of impervious surface aboveground. Raccoons have a preference to remain near an exit suggesting that their presence in SSSs may be exploratory or constrained by food or light availability, although they were found navigating the full extent of some SSSs. \u003cem\u003eMyotis a.\u003c/em\u003e prefer smaller stormwater systems with limited impervious surface disturbance aboveground, particularly in smaller SSSs. We use these findings to discuss ways that construction design and stormwater management can be more wildlife friendly.\u003c/p\u003e","manuscriptTitle":"Identifying the Factors That Influence Raccoon (Procyon Lotor) and Southeastern Myotis (Myotis Austroriparius) Use of Stormwater Sewer Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 20:27:02","doi":"10.21203/rs.3.rs-6355575/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-12T04:03:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-06T08:49:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-22T14:47:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294575735474469234769684902663384684953","date":"2025-04-17T10:36:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292802736283274580823545938372467311812","date":"2025-04-15T09:09:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-11T19:06:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267824697705972903851037434721882344873","date":"2025-04-09T23:40:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26931544906624615146538933596124104155","date":"2025-04-09T14:53:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-09T05:35:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-03T01:55:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-02T09:49:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Urban Ecosystems","date":"2025-04-01T18:53:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"urban-ecosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ueco","sideBox":"Learn more about [Urban Ecosystems](https://www.springer.com/journal/11252)","snPcode":"11252","submissionUrl":"https://submission.nature.com/new-submission/11252/3","title":"Urban Ecosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"370519af-76d0-49b6-bb3c-d9fbe8178822","owner":[],"postedDate":"April 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T15:58:31+00:00","versionOfRecord":{"articleIdentity":"rs-6355575","link":"https://doi.org/10.1007/s11252-025-01748-w","journal":{"identity":"urban-ecosystems","isVorOnly":false,"title":"Urban Ecosystems"},"publishedOn":"2025-05-29 15:56:53","publishedOnDateReadable":"May 29th, 2025"},"versionCreatedAt":"2025-04-14 20:27:02","video":"","vorDoi":"10.1007/s11252-025-01748-w","vorDoiUrl":"https://doi.org/10.1007/s11252-025-01748-w","workflowStages":[]},"version":"v1","identity":"rs-6355575","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6355575","identity":"rs-6355575","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}