Sensory interference displaces an acoustically specialized predator

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Habib, Julianna M. A. Jenkins, Natalie M. Rugg, Guillermo Alvarez-Nuñez, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7483314/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Ambient acoustic conditions shape how animals perceive and interact with their environments, yet their role in structuring space use remains underexplored. Noise can mask biologically informative sounds which can impact foraging success, physiological fitness, and displace animals from viable habitat. Here, we test how landscape variables and soundscape characteristics across a mixed-use forest landscape affect spatial distribution of a nocturnal predator with specialized hearing. We used passive acoustic recordings to assess the effects of noise levels within biologically relevant frequency ranges on landscape use of the northern saw-whet owl ( Aegolius acadicus ) across 276 sites in Oregon, USA. Owl landscape use declined with increasing noise levels in the 1.60–7.10 kHz band, corresponding with species peak auditory sensitivity. In contrast, general low-frequency sound (0.25-1.00 kHz) was a poor predictor of landscape use but negatively affected nightly detection probability. These results provide evidence that sensory masking from ambient soundscapes can constrain the realized acoustic niche and drive acoustic displacement. Our findings highlight the importance of considering full-spectrum acoustic environments in spatial ecology and suggest that species distributions are shaped not only by physical habitat but also by the perceptual accessibility of ecological information. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Zoology Anthropogenic noise sensory ecology occupancy modeling passive acoustic monitoring northern saw-whet owl acoustic displacement Figures Figure 1 Figure 2 Figure 3 Introduction Acoustic conditions are a fundamental, but often overlooked, dimension of animal habitats. The ambient soundscape, composed of geophony (e.g., wind, water), biophony (e.g., insects, birds, amphibians), and anthrophony (e.g., traffic, machinery), influences how animals detect prey, avoid predators, communicate, and navigate [ 1 – 3 ] . These soundscape components are often spatially and temporally dynamic and may vary considerably across structurally similar habitats. While anthropogenic noise has received the most attention as a disruptive force, natural and biological acoustic signals can also generate masking effects, particularly in species with narrow or highly tuned hearing ranges [ 4 – 6 ] . Despite this complexity, ecological models rarely incorporate the sensory accessibility of habitat and instead assume that structurally suitable areas are functionally usable. However, this assumption fails for specialized species that rely on sound to interact with their environment. In such cases, landscape use may be constrained not by resource availability or physical barriers, but by the acoustic properties of the environment relative to the species’ sensory system [ 7 ] . The field of sensory ecology emphasizes that information is central to survival and fitness, and that environmental conditions that interfere with perception can impose ecological costs as significant as habitat loss or fragmentation [ 8 , 9 ] . For auditory dependent species, masking from background noise—regardless of its source—can reduce signal detection, compromise foraging or mating success, and alter patterns of space use and persistence. [ 10 , 11 ] Studies have documented vocal compensation behaviors—such as increasing amplitude or shifting pitch—in birds, frogs, and marine mammals exposed to noise [ 12 , 13 ] . However, such compensation may be unavailable to species that use passive auditory cues, such as most predators. For many organisms, anthropogenic noise directly competes with ecologically important sounds, like prey rustling or conspecific calls, without an opportunity for adjustment. When noise overlaps with the frequency range or detection thresholds of these species, it can reduce foraging success, displace individuals, or exclude populations from noisy areas [ 14 , 15 ] . From a biological perspective, the effects of noise depend not only on its intensity, but on its spectral characteristics and temporal structure, relative to the perceptual system of the organism. For instance, low-frequency traffic noise may have negligible impact on the communication of species that vocalize or hear in high frequencies but could severely impair those with overlapping sensitivity. This highlights the need to treat noise as a general disturbance and align its measurement with the sensory ecology of the species being studied [ 3 , 16 ] . We argue the need to evaluate resource use within the organism’s realized acoustic niche, the portion of the soundscape where ambient sound conditions allow an organism to perceive critical information about its environment without interference from other species. This niche is defined not only by signal transmission, but also signal reception, shaped by both background noise and species-specific auditory sensitivity [ 17 ] . Just as thermal or hydric tolerances define physiological niche boundaries, auditory pressures in the environment may determine where a species can function ecologically. The realized acoustic niche thus complements existing niche frameworks and calls for the integration of sensory constraints into models of habitat suitability. The process whereby species are excluded from parts of their structurally suitable habitat due to masking or interference from noise can be considered acoustic displacement. In this view, habitat loss is not solely a matter of vegetation change, fragmentation, or competitive interactions—it can occur when sensory interference prevents organisms from effectively using space. We describe sensory interference, a driver of acoustic displacement, as an inability to receive vital sensory information due to interference from overlapping acoustic signals (Fig. 1 ). This is particularly important for species with narrow auditory bandwidths or frequency sensitivity, who may be especially vulnerable to acoustic displacement. The potential for acoustic displacement is widespread, yet empirical demonstrations remain rare—largely because studies have lacked access to both fine-scale noise data and species-specific hearing profiles. The northern saw-whet owl ( Aegolius acadicus; hereafter saw-whet owl) offers a compelling model system for testing this hypothesis. This small, nocturnal forest owl is acoustically sensitive and broadly distributed across North America and relies on passive acoustic detection to locate small mammal prey [ 18 , 19 ] . It exhibits extreme auditory specialization, including pronounced ear asymmetry and sensitivity concentrated between 1.6 and 7.1 kHz, with a peak near 4 kHz [ 18 , 20 , 21 ] . These traits support exceptional foraging precision but also make the species vulnerable to mid-frequency noise interference, particularly from railways, machinery, and other sources common in human-modified forests [ 9 , 19 ] . Passive acoustic monitoring (PAM) is a burgeoning method for monitoring nocturnal and elusive soniferous species with the benefit of being a non-invasive tool to monitor wildlife populations that simultaneously gathers useful metrics for soundscape research. Here, we use > 100,000 hours of PAM recordings across 276 forested sites in a low-to-mid elevation industrial forest with a mixed coniferous landscape, to evaluate whether ambient noise within biologically relevant frequency bands predicts variation in landscape use by saw-whet owls. Using a convolutional neural network [ 22 ] to detect owl calls and site-level noise metrics derived from third-octave band analysis, we applied single-season occupancy models to estimate landscape use and detection probability. We specifically tested whether noise levels in the owl’s sensitive frequency band (1.60–7.10 kHz) were more predictive of landscape use than broadband low-frequency noise (0.25–1.00 kHz), while accounting for vegetation structure, terrain, and detectability. We hypothesized that saw-whet owl landscape use would decrease with proximity to transportation infrastructure and higher levels of noise in the most sensitive hearing frequencies of the saw-whet owl. Results We collected and processed 105,673.10 hours of audio from 276 autonomous recording units (ARUs) from March 2nd to September 21st, 2021, in a mixed ownership forest landscape in southwestern Oregon, USA (Fig. 2 ). ARUs recorded for approximately 9 weeks (44.37 ± 0.10 days; mean ± SE). We used a convolutional neural network, PNW-Cnet v4, to identify saw-whet owl territorial ‘toot’ vocalizations from audio [ 22 ] . We manually validated saw-whet owl vocalizations at the site level and confirmed presence at 163 sites for a naïve station use rate of 0.59. At sites with confirmed presence, we adjusted predicted counts of nighttime detections by an area-specific classifier precision of 0.86. We used a threshold of 2 apparent detections to assign daily presence in detection histories to limit false positives. Within those sites, we found 113,584 apparent detections of saw-whet owl territorial calls after adjusting for classifier precision. Occupied stations detected owls for 9.07 ± 0.73 days (mean ± SE) with 1–857 apparent detections per day. The final candidate model set included a diversity of covariate structures for detection probability ( p ) and landscape use (ψ) parameters (Table S3). The most-supported model [ p (LOWFREQNOISE + PRECIP + DATE), ψ(BRDLF 400 + ELEV + STRMDIST + RAILROAD + OWLFREQ + SMCON 200 )] held 61% of the model weight and outperformed the second-ranked model by 2.34 AICc (Table 1 ). The six top-ranked models (95% of the cumulative model weight) differed only in ψ sub-model structure (Table 1 ). A Pearson chi-squared goodness-of-fit test with 1000 bootstraps indicated adequate fit (p-value of 0.46) and no evidence of overdispersion ( \(\:\widehat{c}\) =1.01) [ 23 ] for our top-ranked model. Table 1 Most-supported single species occupancy models (≥ 0.95 of cumulative model weight) for northern saw-whet owl landscape use (ψ) and detection probability (p) ranked by Akaike’s information criterion for small sample sizes (ΔAICc), including Akaike’s model weight (w), number of parameters (k), and the twice negative log-likelihood (-2LogL). See supplemental materials for full candidate model sets (Tables S1–S3). Detection model structure for all top models was p (LOWFREQNOISE + PRECIP + DATE). Model k -2LogL ΔAICc a w ψ(BRDLF400 + ELEV + STRMDIST + RAILROAD + OWLFREQ + SMCON200) 11 7309.51 0.00 0.61 ψ(BRDLF400 + ELEV + STRMDIST + RAILROAD + SMCON200) 10 7314.02 2.34 0.19 ψ(BRDLF400 + ELEV + STRMDIST + RAILROAD + OWLFREQ) 10 7315.68 4.00 0.08 ψ(BRDLF400 + ELEV + STRMDIST + RAILROAD) 9 7319.26 5.43 0.04 ψ(BRDLF400 + ELEV + STRMDIST + OWLFREQ) 9 7321.08 7.24 0.02 ψ(BRDLF400 + ELEV + RAILROAD + OWLFREQ) 9 7322.24 8.40 0.01 Abbrv: SMCON 200 = proportion of small conifers within 200 m, BRDLF 400 = proportion of broadleaf trees within 400 m, RAILROAD = distance to nearest railroad (km), STRMDIST = distance to nearest stream (m), OWLFREQ = mean noise values within the range of 1.6-7.l kHz (dBFS), ELEV = elevation (m), PRECIP = levels of daily precipitation (mm), DATE = Julian date, LOWFREQNOISE = mean nightly noise values calculated from 250–1000 Hz (dBFS). a The AICc of the top ranked model was 7332.512. Daily detection probability of saw-whet owl vocalizations was negatively associated with precipitation, low-frequency noise, and date (Table 2 ). One standard deviation (4.72 dBFS) increase in low-frequency noise was associated with a 33% decrease in odds of detection. While one standard deviation (2.66 mm) increase in daily precipitation was associated with a 10% decrease in the odds of detection. The odds of detecting a saw-whet owl decreased by 47% when surveying 32 days later in the breeding season (Table 2 ). Table 2 Covariate coefficients (β) from top single-species occupancy model for the northern saw-whet owl landscape use (ψ) and detection likelihood ( p ) ranked by Akaike’s information criterion for small sample sizes, including standard error (SE) the lower and upper 95% confidence intervals (LCL; UCL). All variables were scaled to have a mean of 0 and standard deviation of 1 prior to analysis. Parameter Variable a β SE LCL UCL p Intercept -1.67 0.04 -1.74 -1.592 p PRECIP -0.10 0.03 -0.16 -0.03 p DATE -0.63 0.04 -0.71 -0.56 p LOWFREQNOISE -0.40 0.04 -0.48 -0.32 ψ Intercept 0.44 0.14 0.17 0.71 ψ BRDLF 400 -0.52 0.17 -0.85 -0.19 ψ ELEV -0.42 0.16 -0.73 -0.12 ψ STRMDIST 0.38 0.18 0.02 0.74 ψ RAILROAD -0.36 0.18 -0.70 -0.01 ψ OWLFREQ -0.30 0.14 -0.58 -0.02 ψ SMCON 200 0.37 0.15 0.07 0.67 a SMCON 200 = proportion of small conifer within 200 m, BRDLF 400 = proportion of broadleaf trees within 400 m, RAILROAD = distance to nearest railroad (km), STRMDIST = distance to nearest stream (m), OWLFREQ = mean noise values within the range of 1.6-7.l kHz. ELEV = elevation (m), DATE = Julian date, LOWFREQNOISE = mean nightly noise values calculated from 250–1000 Hz (dBFS), PRECIP = amount of daily precipitation (mm). In addition to strong associations with covariates describing forest and topographical conditions, the top-ranked model of saw-whet owl landscape use (ψ) included a strong negative association with the level of sound within the high sensitivity range of saw-whet owl hearing (OWLFREQ; Table 2 ; Fig. 3 ). Each standard deviation increase in OWLFREQ (2.25 dBFS), decreased the odds of landscape use by 26% (Fig. 3 ). Saw-whet owl landscape use was also negatively associated with distance to railroad, such that each standard deviation increase in distance (11.55 km), decreased the odds of landscape use by 30% (Table 2 ; Fig. 3 ). Conversely, landscape use was positively associated with distance to stream, where an increase of 370 m (one standard deviation) increased the odds of landscape use by 46% (Table 2 ; Fig. 3 ), suggesting that saw-whets may have been attracted to the railroad corridor but avoided areas near streams. Notably there was only one major railway in our study area, which travels through Cow Creek Canyon alongside a large creek (Fig. 2 ). Saw-whet owl landscape use was negatively associated with elevation (ELEV); where each standard deviation increase (198.63 m) decreased the odds of landscape use by 34.3% (Table 2 , Fig. 3 ). Landscape use was negatively associated with proportion of broadleaf trees within a 400-m buffer (BRDLF 400 ) and positively associated with proportion of small conifers within a 200-m buffer (SMCON 200 ; Table 2 ). Each standard deviation increase in BRDLF 400 (0.07 m 2 /ha) decreased the odds of landscape use by 41%, while each standard deviation increase in SMCON 200 (0.20 m 2 /ha) increased the odds of landscape use by 45% (Table 2 , Fig. 3 ). Discussion Our findings provide evidence that variation in ambient noise (including geophony, biophony, and anthrophony) across the acoustic spectrum can influence the spatial ecology of an acoustically specialized predator. Saw-whet owl landscape use declined significantly with increasing sound levels within the 1.60–7.10 kHz frequency bands, aligning with the species’ peak auditory sensitivity [ 18 , 20 ] . This frequency-specific effect was not explained by structural habitat features alone, indicating that perceptual accessibility constrains landscape use. These results offer empirical support for the concept of the realized acoustic niche, a sensory-defined space within which organisms can successfully detect, process, and respond to ecologically meaningful cues [ 3 , 7 ] . Even in structurally suitable environments, species may be displaced when ambient sound conditions interfere with their perceptual abilities. We build on this framework by demonstrating how acoustic displacement arises when sound levels in species-relevant frequency bands mask key ecological information, reducing the functional extent of otherwise viable habitat [ 11 , 15 ] . Importantly, the acoustic displacement effect we observed was not solely attributable to anthropogenic sources. Although many studies in soundscape ecology emphasize roads, aircraft, or urban noise, our study area featured complex acoustic inputs from natural and biological sources. Insects, amphibians, flowing water, and wind all contributed to the mid-frequency background noise measured in our study. Our results emphasize that ecological effects of masking are determined less by source category and more by spectral overlap and spatial-temporal consistency of the sound [ 1 , 6 ] . This perspective recognizes that animals evolved under geophonic and biophonic acoustic pressures and that anthropogenic sounds may add to, rather than replace, existing background noise. In some cases, interactions among sources may have cumulative or non-additive effects on masking or behavioral response [ 9 ] . Saw-whet owls rely on passive detection of conspecific vocalizations and prey-generated cues, such as rustling in leaf litter. Their specialized auditory system includes asymmetric ears and fine frequency discrimination in the 1.60–7.10 kHz range, with peak sensitivity near 4.00 kHz [ 18 ] . These traits allow high foraging precision but increase susceptibility to masking in environments where sound levels are elevated in the same frequency range. In contrast to shifting signal amplitude or frequency to compensate for noise, passive-listening predators have fewer compensatory mechanisms [ 12 , 19 ] . Our finding that mid-frequency noise was a better predictor of landscape use than broadband low-frequency noise underscores that spectral specificity is essential in understanding sensory constraints on space use. While increased distance to railroads had a small yet significant negative effect on landscape use, this variable may reflect a combination of acoustic and structural influences. Rail corridors can generate high-amplitude yet intermittent noise, but may also support edge habitats or small mammal populations that provide foraging opportunities [ 24 ] . The irregularity of this noise may allow periods of auditory recovery or detection, although it may also create unpredictable sensory environments that increase cognitive or energetic costs [ 15 ] . Future research could explore whether predictability, amplitude modulation, and periodicity of sound sources mediate the effects of masking across species and behaviors. Vegetation structure and topography influenced landscape use in expected directions. Areas with higher proportion of small conifers were more likely to be occupied, consistent with previous studies of saw-whet owl nesting and foraging preferences [ 25 ] . Elevation had a negative effect, possibly reflecting either prey availability or changing vegetation profiles. Broadleaf proportion was negatively associated with landscape use, which may be due to differences in sound attenuation or prey density in those habitats. Distance to streams had a negative effect, possibly reflecting increased noise levels or different vegetation profiles near riparian areas. Methodologically, our study demonstrates the value of integrating passive acoustic monitoring with machine learning and species-specific sensory ecology. Automated classifiers such as PNW-Cnet v4 allow large-scale, repeatable detection of vocalizations for many species, enabling spatial replication at a level difficult to achieve through traditional field methods [ 26 ] . When combined with frequency-targeted acoustic metrics and robust occupancy modeling frameworks, this approach facilitates direct testing of hypotheses about how soundscapes influence species distributions and richness [ 27 , 28 ] . As passive acoustic monitoring tools become more accessible, opportunities to quantify perceptual constraints across taxa and ecosystems will expand. The concept of acoustic displacement applies not only to owls but to any species that relies on acoustic cues for ecological decision-making. This includes bats that echolocate in narrow frequency bands, frogs that use tonal mating calls, and marine mammals that depend on underwater sound for navigation and foraging [ 7 , 29 , 30 ] . In each case, the overlap between ambient noise and auditory sensitivity determines whether sensory information is lost or can be accessed. The generality of this mechanism suggests that sensory-based displacement may be widespread but underutilized in analyses of spatial ecology. Our results also point to practical implications for conservation. Land managers and conservation planners often rely on structural habitat maps to identify suitable areas for protection or mitigation [ 31 ] . However, if perceptual access is impaired, these maps may overestimate functional habitat. For acoustic specialists, it may be necessary to evaluate soundscape conditions within biologically relevant frequency bands as a criterion for habitat quality [ 7 ] . Conservation strategies might then include not only habitat restoration but also noise mitigation, timing of human activities, or establishment of acoustic refugia [ 10 , 11 ] . Future work could extend these findings by quantifying the effect of acoustic masking to reproductive success, energy expenditure, and prey capture rates. Multi-season models incorporating survival or breeding data could help evaluate whether acoustic displacement has population-level impacts. Additionally, classification of noise sources and spectral structure at higher resolutions would allow researchers to disentangle the relative contributions of different soundscape components. Integrating spatial soundscape models with species-specific detections and behavioral data represents a promising frontier for landscape ecology and conservation science. We found rare evidence that ambient noise, including both natural and anthropogenic components, can influence a species’ use of structurally suitable areas by interfering with its ability to perceive its environment. By demonstrating that sensory constraints shape the realized acoustic niche, we highlight the need to consider not just where animals can survive, but where they can hear. This perspective broadens our understanding of how species interact with dynamic soundscapes and offers a conceptual and methodological foundation for incorporating sensory ecology into spatial modeling, conservation planning, and biodiversity assessment. Methods Study Area We collected audio data in spring and summer of 2021, within the saw-whet owl’s breeding season, across a 1,422-km 2 area in southwestern Oregon (Fig. 2 ) as part of a broad scale PAM program designed for population monitoring of northern spotted owls ( Strix occidentalis caurina ) [ 26 ] . This area contains mixed land ownership in a checkerboard of federally managed (U.S. Bureau of Land Management) and private lands. The area is part of the Klamath Mountain region, with a low-to-mid elevation mixed-conifer landscape used for natural resources, including logging, farming, and mining. The Klamath transportation network contains logging roads, secondary highways, and two major transportation networks: Interstate 5 and the Central Oregon and Pacific railroad line that travels through Cow Creek Canyon. Data collection We used acoustic data collected from 276 monitoring stations within 69 randomly selected 5-km 2 hexagons (Fig. 2 ) [ 26 ] . Approximately 20% of available hexagons (hexagons with ≥ 25% federal ownership and ≥ 50% forest capable) were randomly selected from a tessellation across the area. Each hexagon was sampled using four ARUs (SM4 Wildlife Acoustics, Maynard, MA, USA). Each ARU was deployed on mid to upper slopes, ≥ 500 m from other ARUs, and ≥ 50 m from roads, trails, and streams to reduce excessive noise. ARUs were deployed on small trees for 6–8 weeks. We programmed ARUs to record for four hours during the crepuscular periods and ten minutes on-the-hour every remaining hour of the night and day [ 26 ] . The ARUs have the same sensitivity as human hearing, and their listening radius is impacted by factors limiting sound propagation, such as vegetation, terrain, wind, and rain. ARUs had a signal-to-noise ratio of 80 dB typical at 1 kHz, a sampling rate of 32,000 Hz, and a recording bandwidth of 20 Hz to 48 kHz at decibel levels of -33.5 dB to 122 dB. Audio Processing and Validation We processed audio data using a convolutional neural network (PNW-Cnet v4) trained to identify various acoustic signals using 12-second spectrograms, including the territorial “toot” song of the saw-whet owl [ 22 , 32 ] . To assess the performance of PNW-Cnet v4 for saw-whet owl signals relative to our study area, we randomly selected 200 audio clips with a PNW-Cnet classification score of ≥ 0.95 for manual validation. We estimated classification precision as the number of manually confirmed saw-whet owl calls divided by the total reviewed. We used each calendar date (i.e., 24-hr period between 00:00 and 23:59) as survey occasions, up to 45 dates per recording station, omitting daytime audio (08:15 to 19:30) when saw-whet owls are unlikely to vocalize. We generated counts of daily apparent nighttime detections with a classification score of ≥ 0.95 adjusted by our PNW-Cnet v4 precision and used a threshold of the 0.25 quantile of daily detections to assign survey presence to limit false positives. We manually confirmed presence/absence of saw-whet owl calls at all recording stations. If an occasion did not meet the threshold of apparent detections but had ≥ 1 manually confirmed detection(s), then we assigned a positive detection in that survey occasion. If we confirmed zero detections at a recording station, all survey occasions at the station were assigned non-detection, regardless of the number of PNW-Cnet v4 apparent detections. Survey covariates We selected survey covariates which we predicted could affect sound attenuation or saw-whet owl calling behavior (Table S4). We used survey occasion Julian date (DATE) to examine if detection probability changed over the season. We hypothesized that high levels of nightly low-frequency noise would negatively impact our ability to detect owl calls. We calculated low-frequency noise levels from 250–1000 Hz in decibels relative to full scale (dBFS) using Kaleidoscope Pro third-octave sound level analysis [ 33 ] to capture sound levels which may mask saw-whet owl calls (LOWFREQNOISE). We further hypothesized that weather conditions may affect owl calling behavior [ 34 ] , thus we evaluated daily precipitation (PRECIP) and average daily temperature (TEMP) [ 35 ] . Site covariates We calculated site-specific covariates within 200-, 400- and 600-m buffers around ARU locations to evaluate multi-scale variation in landscape use between stations (Table S4) [ 36 ] . There is limited information about the home range size of non-migratory populations of saw-whet owls in the Pacific Northwest, so we based our buffer sizes on the likely listening radii of the recording units. We used ArcGIS Pro [ 37 ] and remotely sensed data to calculate landscape and terrain covariates (RUGGED, ELEV, TOPO). We used data from a gradient nearest neighbor dataset [ 38 ] to generate forest structure covariates (SNAG, STNDHGHT, CANCOV, OGSI80, BRDLF, SMCON; Table S4). Using data from the Oregon Department of Transportation, we calculated the distance from each recording station to the nearest railroad (RAILROAD). We also calculated the distance from each station to the closest unpaved road, secondary highway, and major highway (UNPAVEDRD, SCNDHWY, MJRHWY). We used data from the National Hydrography Dataset (Version 2.1) to nearest flowlines and non-ephemeral waters (STRMDIST). We hypothesized that loud noise within the most sensitive frequencies of the saw-whet owl’s hearing range would lead to decreased landscape use and that as the number of loud nights increased, landscape use would decrease. In addition to summarizing seasonal low-frequency noise levels (LOWFREQNOISE), we calculated noise covariates based on the most sensitive frequencies of the saw-whet owl’s hearing range [ 18 ] . We ran third-octave sound level analyses [ 33 ] on frequencies between 1.6–7.1 kHz (OWLFREQ) and their frequency of best sensitivity, 4 kHz (BESTHZ). We averaged sound level values across nightly audio files for each station. To further investigate if persistent ‘loud’ nights within the low-frequency noise levels and the saw-whet owl’s sensitive hearing frequencies impacted landscape use [ 39 ] , we summarized the proportion of survey occasions where noise levels were above the 25% percentile within both low frequencies and the most sensitive areas of the owls hearing range (LOW_LOUD, OWL_LOUD). We used the scale function in R to standardize covariates. Data Analysis We fit single-season, single-species occupancy models in package RPresence [ 40 ] to examine saw-whet owl landscape use (ψ) while incorporating variation in daily nighttime detection probability ( p ) [ 41 ] . We used a secondary candidate-set strategy for model selection [ 42 ] and evaluated model support using Akaike’s Information Criterion corrected for small sample sizes (AICc). We developed sub-model sets independently, holding non-focal parameters constant while testing candidate covariates on the focal parameter, progressing to multivariate models using well supported covariates (Table S1 –S2). The final candidate model set included combinations of all parameter sub-models within 10 AICc units of the top-ranking sub-model that did not add uninformative parameters (Table S3) [ 43 ] . We did not include correlated variables within sub-models (|r| >0.6; Table S5–S6). We considered coefficient estimates (β) whose 95% confidence intervals (CI) did not include zero as supported. We used a Pearson chi-squared goodness-of-fit test with 1000 bootstraps to evaluate model fit and test for overdispersion [ 23 ] . Declarations Funding: We thank Oakridge Institute of Science and Education, U.S. Bureau of Land Management, U.S. Forest Service, and the Pacific Northwest Research Station for providing funding for this research. The findings and conclusions in this article are those of the authors and do not represent the views of the U.S. Forest Service or the U.S. Government. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Acknowledgements We thank the many field and lab technicians for their work gathering these data. Special thanks to A. Gasc, C. Gates, T. J. Mohler, C. R. Habib, L. R. Habib, H. Hester, K. E. Parker, M. Alsaid, and H. L. Basile for your support with noise analysis, maps, edits, and our many research questions. Author Contributions A. R. Habib lead the formal analysis, conceptualization, investigation, and wrote the original draft and contributed to data curation, methodology, review and editing of the manuscript, software, validation, and visualization. N. M. Rugg and J. M. A. Jenkins contributed to conceptualization, data curation, formal analysis, methodology, software, validation, and review and editing of the manuscript. J. M. A. Jenkins, N. M. Rugg, and D. B. Lesmeister supervised the project and analysis. G. Nuñez-Alvarez contributed to reviewing and editing of the manuscript and visualization. D. B. Lesmeister supported conceptualization and methodology and provided funding acquisition, project administration, resources, and review and editing of the manuscript. Competing interests statement To the best of our knowledge, the named authors have no conflict of interest, financial or otherwise. Data Availability The full dataset necessary to run these analyses are publicly available on Zenodo through the following DOI: 10.5281/zenodo.16944516 References Pijanowski, B. C. et al. Soundscape Ecology: The Science of Sound in the Landscape. BioScience 61 , 203–216 (2011). Duarte, M. H. L. et al. 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Chronic anthropogenic noise disrupts glucocorticoid signaling and has multiple effects on fitness in an avian community. Proc. Natl. Acad. Sci. U.S.A. 115, (2018). Morley, E. L., Jones, G. & Radford, A. N. The importance of invertebrates when considering the impacts of anthropogenic noise. Proc. R. Soc. B. 281, 20132683 (2014). Krause, B. The Niche Hypothesis:: A virtual symphony of animal sounds, the origins of musical expression and the health of habitats. Soundscape Newsletter (World Forum for Acoustic Ecology) (1993). Beatini, J. R., Proudfoot, G. A. & Gall, M. D. Frequency sensitivity in Northern saw-whet owls (Aegolius acadicus). J. Comp. Physiol. A . 204 , 145–154 (2018). Mason, J. T., McClure, C. J. W. & Barber, J. R. Anthropogenic noise impairs owl hunting behavior. Biol. Conserv. 199 , 29–32 (2016). De Koning, M., Beatini, J. R., Proudfoot, G. A. & Gall, M. D. Hearing in 3D: Directional Auditory Sensitivity of Northern Saw-Whet Owls ( Aegolius acadicus ). Integr. Comp. Biol. 60 , 1058–1067 (2020). Norberg, R. A. & Norberg, R. A. Occurrence and independent evolution of bilateral ear asymmetry in owls and implications on owl taxonomy. Philosophical Trans. Royal Soc. Lond. B Biol. Sci. 280 , 375–408 (1977). Ruff, Z. J., Lesmeister, D. B., Jenkins, J. M. A. & Sullivan, C. M. PNW-Cnet v4: Automated species identification for passive acoustic monitoring. SoftwareX 23 , 101473 (2023). MacKenzie, D. I. & Bailey, L. L. Assessing the fit of site-occupancy models. JABES 9 , 300–318 (2004). Wiącek, J., Polak, M., Filipiuk, M., Kucharczyk, M. & Bohatkiewicz, J. Do Birds Avoid Railroads as Has Been Found for Roads? Environ. Manage. 56 , 643–652 (2015). Swengel, S. R. & Swengel, A. B. Roosts of Northern Saw-Whet Owls in Southern Wisconsin. Condor 94 , 699–706 (1992). Lesmeister, D. B. & Jenkins, J. M. A. Integrating new technologies to broaden the scope of northern spotted owl monitoring and linkage with USDA forest inventory data. Front. Glob Change . 5 , 966978 (2022). Robert, A. et al. The theory of island biogeography and soundscapes: Species diversity and the organization of acoustic communities. J. Biogeogr. 46 , 1901–1911 (2019). Mullet, T. C., Farina, A. & Gage, S. H. The Acoustic Habitat Hypothesis: An Ecoacoustics Perspective on Species Habitat Selection. Biosemiotics 10 , 319–336 (2017). Domer, A. et al. Adverse effects of noise pollution on foraging and drinking behaviour of insectivorous desert bats. Mamm. Biol. 101 , 497–501 (2021). Duarte, C. M. et al. The soundscape of the Anthropocene ocean. Science 371 , eaba4658 (2021). Adamo, M. et al. Habitat mapping of coastal wetlands using expert knowledge and Earth observation data. J. Appl. Ecol. 53 , 1521–1532 (2016). Ruff, Z. J., Lesmeister, D. B., Appel, C. L. & Sullivan, C. M. Workflow and convolutional neural network for automated identification of animal sounds. Ecol. Ind. 124 , 107419 (2021). Wildlife Acoustics. Kaleidoscope Pro: Version 5.4. Wildlife Acoustics, Inc. Wildlife Acoustics (2021). https://www.wildlifeacoustics.com/products/kaleidoscope-pro Palmer, D. A. Annual, Seasonal, and Nightly Variation in Calling Activity of Boreal and Northern Saw-Whet Owls in Biology and Conservation of Northern Forest Owls (U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1987). PRISM Climate Group. Parameter-elevation Regions on Independent Slopes Models, Gridded Climate Data. https://prism.oregonstate.edu/ Cushman, S. A. et al. Simulating multi-scale optimization and variable selection in species distribution modeling. Ecol. Inf. 83 , 102832 (2024). ESRI & ArcGIS Pro Release 2.9.5. Environmental Systems Research Institute Redlands, California, USA. (2022). https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview Bell, D. M., Gregory, M. J., Palmer, M. & Davis, R. Guidance for Forest Management and Landscape Ecology Applications of Recent Gradient Nearest Neighbor Imputation Maps in California, Oregon, and Washington . PNW-GTR-1018 https://www.fs.usda.gov/research/treesearch/67105 (2023). 10.2737/PNW-GTR-1018 Hayward, L. S., Bowles, A. E., Ha, J. C. & Wasser, S. K. Impacts of acute and long-term vehicle exposure on physiology and reproductive success of the northern spotted owl. Ecosphere 2, art65 (2011). MacKenzie, D. I., Hines, J. E. & RPresence R Interface for Program PRESENCE. www (2023). .mbr-pwrc.usgs.gov/software/presence.html MacKenzie, D. I. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence (Academic Press, an imprint of Elsevier, 2018). Morin, D. J. et al. Is your ad hoc model selection strategy affecting your multimodel inference? Ecosphere 11, e02997 (2020). Arnold, T. W. Uninformative Parameters and Model Selection Using Akaike’s Information Criterion. J. Wildl. Manage. 74 , 1175–1178 (2010). Habib, A. Figure 1.Created in BioRender. (2025). https://BioRender.com/t9xixd9 . Additional Declarations No competing interests reported. Supplementary Files Habibetal.Sensoryinterferencedisplaces.pdf Cite Share Download PDF Status: Published Journal Publication published 04 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 31 Oct, 2025 Reviews received at journal 29 Oct, 2025 Reviews received at journal 23 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviewers agreed at journal 02 Oct, 2025 Reviewers agreed at journal 02 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 08 Sep, 2025 Editor assigned by journal 08 Sep, 2025 Editor invited by journal 05 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 02 Sep, 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-7483314","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":512931715,"identity":"7732acb6-81b9-450f-94b2-046543bd7c23","order_by":0,"name":"Aleena R. Habib","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYDACdgiVwCDB2MbwAchiYyekhRlJC+MMkBZm4rUAFfMgieAE/M3MBx8X1NzL45/d3PbY5tc2eT5mBsYPH3Nwa5E4zJZsPONYcbHEnYPtxrl9tw3bmBmYJWduw2PNYR4zaR62hMSGG4lt0rk9txmBWtiYefFokT/M//03z7+ExPkgLZY9t+0JajE4zANU0JaQuAGkheHH7USCWgwPsxlL8/YlJG4E+sWwt+F2chszYzNev8gdb374medbQuK82+3PHvz4c9t2fnvzwQ8f8XkfBQATAIhsIFY9CPwhRfEoGAWjYBSMFAAAFMtRUgcN75sAAAAASUVORK5CYII=","orcid":"","institution":"Oak Ridge Institute for Science and Education","correspondingAuthor":true,"prefix":"","firstName":"Aleena","middleName":"R.","lastName":"Habib","suffix":""},{"id":512931716,"identity":"6d71a8b1-ba0b-4c24-a8a8-b70ace104ff7","order_by":1,"name":"Julianna M. A. Jenkins","email":"","orcid":"","institution":"Pacific Northwest Research Station, USDA Forest Service","correspondingAuthor":false,"prefix":"","firstName":"Julianna","middleName":"M. A.","lastName":"Jenkins","suffix":""},{"id":512931717,"identity":"66176400-63c7-4868-933f-419c2e445609","order_by":2,"name":"Natalie M. Rugg","email":"","orcid":"","institution":"Pacific Northwest Research Station, USDA Forest Service","correspondingAuthor":false,"prefix":"","firstName":"Natalie","middleName":"M.","lastName":"Rugg","suffix":""},{"id":512931718,"identity":"45ad7e5c-650e-429f-b535-426cea5a92e9","order_by":3,"name":"Guillermo Alvarez-Nuñez","email":"","orcid":"","institution":"Oak Ridge Institute for Science and Education","correspondingAuthor":false,"prefix":"","firstName":"Guillermo","middleName":"","lastName":"Alvarez-Nuñez","suffix":""},{"id":512931719,"identity":"0987284d-f123-4297-91e8-2d3d8b6592a0","order_by":4,"name":"Damon B. Lesmeister","email":"","orcid":"","institution":"Pacific Northwest Research Station, USDA Forest Service","correspondingAuthor":false,"prefix":"","firstName":"Damon","middleName":"B.","lastName":"Lesmeister","suffix":""}],"badges":[],"createdAt":"2025-08-28 21:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7483314/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7483314/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-30437-z","type":"published","date":"2025-12-04T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91368770,"identity":"c29e1b66-ccba-47f7-bec2-c052ed86012c","added_by":"auto","created_at":"2025-09-15 18:15:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":480644,"visible":true,"origin":"","legend":"\u003cp\u003eIdeal acoustic foraging conditions free of sensory interference where signals from prey can be detected by the owl, leading to successful prey capture (a). Sensory interference from anthropogenic noise interferes with prey detection and causes the owl to leave the area (b). Acoustic displacement of the owl caused by sensory interference (c). Owls abandon otherwise suitable habitats due to unsuitable acoustic conditions for foraging and communication. Light green represents suitable habitat while the dark green represents the realized habitat used due to acoustic displacement (c) Visuals created in Biorender.\u003csup\u003e[44]\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage27.png","url":"https://assets-eu.researchsquare.com/files/rs-7483314/v1/ef6f945b4fbb805520b2632a.png"},{"id":91368772,"identity":"11b1a944-6217-4a80-9c02-6566d605e3ba","added_by":"auto","created_at":"2025-09-15 18:15:46","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":543204,"visible":true,"origin":"","legend":"\u003cp\u003ePassive acoustic sampling of 276 recording sites within 69 5-km\u003csup\u003e2\u003c/sup\u003e hexagonal units in Oregon, USA in 2021. Each randomly selected hexagonal unit was sampled with four autonomous recording stations. Visuals created in ArcGIS Pro \u003csup\u003e[37]\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7483314/v1/2e17ff674d1562978fa7151b.jpeg"},{"id":91369807,"identity":"bd97d0d6-a940-4667-bf5b-b455c1a91a36","added_by":"auto","created_at":"2025-09-15 18:31:46","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":124927,"visible":true,"origin":"","legend":"\u003cp\u003eNorthern saw-whet owl landscape use probability relative effects of covariates in the most-supported single-season, single-species occupancy model: proportion of broadleaf trees within a 400-meter buffer (BRDLF\u003csub\u003e400\u003c/sub\u003e), elevation of the station in meters (ELEV), distance to nearest stream in meter(STRMDISTs, distance to nearest railroad in kilometers (RAILROAD), average nightly frequency levels in decibels relative to full scale between 1.6 and 7.1 kHz (OWLFREQ), and proportion of small conifers within a 200-meter buffer (SMCON\u003csub\u003e200\u003c/sub\u003e). Estimates and 95% confidence limits were generated for each focal covariate while holding other covariates at their mean value.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7483314/v1/2fb603ae43de25189de4e3ec.jpeg"},{"id":97724069,"identity":"1744e357-09d3-4423-88de-5df5272d1fdd","added_by":"auto","created_at":"2025-12-08 16:11:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1739101,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7483314/v1/34b72b8b-9924-441e-897e-70dbab22e9d3.pdf"},{"id":91368774,"identity":"6f004b02-83c0-4ee1-a17f-695cb77b4c61","added_by":"auto","created_at":"2025-09-15 18:15:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":330242,"visible":true,"origin":"","legend":"","description":"","filename":"Habibetal.Sensoryinterferencedisplaces.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7483314/v1/57444e87898c3a5694ab53b9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sensory interference displaces an acoustically specialized predator","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcoustic conditions are a fundamental, but often overlooked, dimension of animal habitats. The ambient soundscape, composed of geophony (e.g., wind, water), biophony (e.g., insects, birds, amphibians), and anthrophony (e.g., traffic, machinery), influences how animals detect prey, avoid predators, communicate, and navigate \u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. These soundscape components are often spatially and temporally dynamic and may vary considerably across structurally similar habitats. While anthropogenic noise has received the most attention as a disruptive force, natural and biological acoustic signals can also generate masking effects, particularly in species with narrow or highly tuned hearing ranges \u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Despite this complexity, ecological models rarely incorporate the sensory accessibility of habitat and instead assume that structurally suitable areas are functionally usable.\u003c/p\u003e\u003cp\u003eHowever, this assumption fails for specialized species that rely on sound to interact with their environment. In such cases, landscape use may be constrained not by resource availability or physical barriers, but by the acoustic properties of the environment relative to the species\u0026rsquo; sensory system \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The field of sensory ecology emphasizes that information is central to survival and fitness, and that environmental conditions that interfere with perception can impose ecological costs as significant as habitat loss or fragmentation \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. For auditory dependent species, masking from background noise\u0026mdash;regardless of its source\u0026mdash;can reduce signal detection, compromise foraging or mating success, and alter patterns of space use and persistence. \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eStudies have documented vocal compensation behaviors\u0026mdash;such as increasing amplitude or shifting pitch\u0026mdash;in birds, frogs, and marine mammals exposed to noise \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. However, such compensation may be unavailable to species that use passive auditory cues, such as most predators. For many organisms, anthropogenic noise directly competes with ecologically important sounds, like prey rustling or conspecific calls, without an opportunity for adjustment. When noise overlaps with the frequency range or detection thresholds of these species, it can reduce foraging success, displace individuals, or exclude populations from noisy areas \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFrom a biological perspective, the effects of noise depend not only on its intensity, but on its spectral characteristics and temporal structure, relative to the perceptual system of the organism. For instance, low-frequency traffic noise may have negligible impact on the communication of species that vocalize or hear in high frequencies but could severely impair those with overlapping sensitivity. This highlights the need to treat noise as a general disturbance and align its measurement with the sensory ecology of the species being studied \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe argue the need to evaluate resource use within the organism\u0026rsquo;s realized acoustic niche, the portion of the soundscape where ambient sound conditions allow an organism to perceive critical information about its environment without interference from other species. This niche is defined not only by signal transmission, but also signal reception, shaped by both background noise and species-specific auditory sensitivity \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Just as thermal or hydric tolerances define physiological niche boundaries, auditory pressures in the environment may determine where a species can function ecologically. The realized acoustic niche thus complements existing niche frameworks and calls for the integration of sensory constraints into models of habitat suitability.\u003c/p\u003e\u003cp\u003eThe process whereby species are excluded from parts of their structurally suitable habitat due to masking or interference from noise can be considered acoustic displacement. In this view, habitat loss is not solely a matter of vegetation change, fragmentation, or competitive interactions\u0026mdash;it can occur when sensory interference prevents organisms from effectively using space. We describe sensory interference, a driver of acoustic displacement, as an inability to receive vital sensory information due to interference from overlapping acoustic signals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This is particularly important for species with narrow auditory bandwidths or frequency sensitivity, who may be especially vulnerable to acoustic displacement. The potential for acoustic displacement is widespread, yet empirical demonstrations remain rare\u0026mdash;largely because studies have lacked access to both fine-scale noise data and species-specific hearing profiles.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe northern saw-whet owl (\u003cem\u003eAegolius acadicus;\u003c/em\u003e hereafter saw-whet owl) offers a compelling model system for testing this hypothesis. This small, nocturnal forest owl is acoustically sensitive and broadly distributed across North America and relies on passive acoustic detection to locate small mammal prey \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. It exhibits extreme auditory specialization, including pronounced ear asymmetry and sensitivity concentrated between 1.6 and 7.1 kHz, with a peak near 4 kHz \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. These traits support exceptional foraging precision but also make the species vulnerable to mid-frequency noise interference, particularly from railways, machinery, and other sources common in human-modified forests \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePassive acoustic monitoring (PAM) is a burgeoning method for monitoring nocturnal and elusive soniferous species with the benefit of being a non-invasive tool to monitor wildlife populations that simultaneously gathers useful metrics for soundscape research. Here, we use\u0026thinsp;\u0026gt;\u0026thinsp;100,000 hours of PAM recordings across 276 forested sites in a low-to-mid elevation industrial forest with a mixed coniferous landscape, to evaluate whether ambient noise within biologically relevant frequency bands predicts variation in landscape use by saw-whet owls. Using a convolutional neural network \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e to detect owl calls and site-level noise metrics derived from third-octave band analysis, we applied single-season occupancy models to estimate landscape use and detection probability. We specifically tested whether noise levels in the owl\u0026rsquo;s sensitive frequency band (1.60\u0026ndash;7.10 kHz) were more predictive of landscape use than broadband low-frequency noise (0.25\u0026ndash;1.00 kHz), while accounting for vegetation structure, terrain, and detectability. We hypothesized that saw-whet owl landscape use would decrease with proximity to transportation infrastructure and higher levels of noise in the most sensitive hearing frequencies of the saw-whet owl.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe collected and processed 105,673.10 hours of audio from 276 autonomous recording units (ARUs) from March 2nd to September 21st, 2021, in a mixed ownership forest landscape in southwestern Oregon, USA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). ARUs recorded for approximately 9 weeks (44.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 days; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE). We used a convolutional neural network, PNW-Cnet v4, to identify saw-whet owl territorial \u0026lsquo;toot\u0026rsquo; vocalizations from audio \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. We manually validated saw-whet owl vocalizations at the site level and confirmed presence at 163 sites for a na\u0026iuml;ve station use rate of 0.59. At sites with confirmed presence, we adjusted predicted counts of nighttime detections by an area-specific classifier precision of 0.86. We used a threshold of 2 apparent detections to assign daily presence in detection histories to limit false positives. Within those sites, we found 113,584 apparent detections of saw-whet owl territorial calls after adjusting for classifier precision. Occupied stations detected owls for 9.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73 days (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE) with 1\u0026ndash;857 apparent detections per day.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe final candidate model set included a diversity of covariate structures for detection probability (\u003cem\u003ep\u003c/em\u003e) and landscape use (ψ) parameters (Table S3). The most-supported model [\u003cem\u003ep\u003c/em\u003e(LOWFREQNOISE\u0026thinsp;+\u0026thinsp;PRECIP\u0026thinsp;+\u0026thinsp;DATE), ψ(BRDLF\u003csub\u003e400\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ELEV\u0026thinsp;+\u0026thinsp;STRMDIST\u0026thinsp;+\u0026thinsp;RAILROAD\u0026thinsp;+\u0026thinsp;OWLFREQ\u0026thinsp;+\u0026thinsp;SMCON\u003csub\u003e200\u003c/sub\u003e)] held 61% of the model weight and outperformed the second-ranked model by 2.34 AICc (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The six top-ranked models (95% of the cumulative model weight) differed only in ψ sub-model structure (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A Pearson chi-squared goodness-of-fit test with 1000 bootstraps indicated adequate fit (p-value of 0.46) and no evidence of overdispersion (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{c}\\)\u003c/span\u003e\u003c/span\u003e =1.01) \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e for our top-ranked model.\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\u003eMost-supported single species occupancy models (\u0026ge;\u0026thinsp;0.95 of cumulative model weight) for northern saw-whet owl landscape use (ψ) and detection probability (p) ranked by Akaike\u0026rsquo;s information criterion for small sample sizes (ΔAICc), including Akaike\u0026rsquo;s model weight (w), number of parameters (k), and the twice negative log-likelihood (-2LogL). See supplemental materials for full candidate model sets (Tables S1\u0026ndash;S3). Detection model structure for all top models was \u003cem\u003ep\u003c/em\u003e(LOWFREQNOISE\u0026thinsp;+\u0026thinsp;PRECIP\u0026thinsp;+\u0026thinsp;DATE).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ek\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2LogL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔAICc\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ew\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ(BRDLF400\u0026thinsp;+\u0026thinsp;ELEV\u0026thinsp;+\u0026thinsp;STRMDIST\u0026thinsp;+\u0026thinsp;RAILROAD\u0026thinsp;+\u0026thinsp;OWLFREQ\u0026thinsp;+\u0026thinsp;SMCON200)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7309.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ(BRDLF400 + ELEV\u0026thinsp;+\u0026thinsp;STRMDIST\u0026thinsp;+\u0026thinsp;RAILROAD\u0026thinsp;+\u0026thinsp;SMCON200)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7314.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ(BRDLF400\u0026thinsp;+\u0026thinsp;ELEV\u0026thinsp;+\u0026thinsp;STRMDIST\u0026thinsp;+\u0026thinsp;RAILROAD\u0026thinsp;+\u0026thinsp;OWLFREQ)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7315.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ(BRDLF400\u0026thinsp;+\u0026thinsp;ELEV\u0026thinsp;+\u0026thinsp;STRMDIST\u0026thinsp;+\u0026thinsp;RAILROAD)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7319.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ(BRDLF400\u0026thinsp;+\u0026thinsp;ELEV\u0026thinsp;+\u0026thinsp;STRMDIST\u0026thinsp;+\u0026thinsp;OWLFREQ)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7321.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ(BRDLF400\u0026thinsp;+\u0026thinsp;ELEV\u0026thinsp;+\u0026thinsp;RAILROAD\u0026thinsp;+\u0026thinsp;OWLFREQ)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7322.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbrv: SMCON\u003csub\u003e200\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;proportion of small conifers within 200 m, BRDLF\u003csub\u003e400\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;proportion of broadleaf trees within 400 m, RAILROAD\u0026thinsp;=\u0026thinsp;distance to nearest railroad (km), STRMDIST\u0026thinsp;=\u0026thinsp;distance to nearest stream (m), OWLFREQ\u0026thinsp;=\u0026thinsp;mean noise values within the range of 1.6-7.l kHz (dBFS), ELEV\u0026thinsp;=\u0026thinsp;elevation (m), PRECIP\u0026thinsp;=\u0026thinsp;levels of daily precipitation (mm), DATE\u0026thinsp;=\u0026thinsp;Julian date, LOWFREQNOISE\u0026thinsp;=\u0026thinsp;mean nightly noise values calculated from 250\u0026ndash;1000 Hz (dBFS).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e The AICc of the top ranked model was 7332.512.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDaily detection probability of saw-whet owl vocalizations was negatively associated with precipitation, low-frequency noise, and date (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). One standard deviation (4.72 dBFS) increase in low-frequency noise was associated with a 33% decrease in odds of detection. While one standard deviation (2.66 mm) increase in daily precipitation was associated with a 10% decrease in the odds of detection. The odds of detecting a saw-whet owl decreased by 47% when surveying 32 days later in the breeding season (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\u003eCovariate coefficients (β) from top single-species occupancy model for the northern saw-whet owl landscape use (ψ) and detection likelihood (\u003cem\u003ep\u003c/em\u003e) ranked by Akaike\u0026rsquo;s information criterion for small sample sizes, including standard error (SE) the lower and upper 95% confidence intervals (LCL; UCL). All variables were scaled to have a mean of 0 and standard deviation of 1 prior to analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLCL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUCL\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePRECIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDATE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOWFREQNOISE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBRDLF\u003csub\u003e400\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eELEV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSTRMDIST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRAILROAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOWLFREQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eψ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSMCON\u003csub\u003e200\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e SMCON\u003csub\u003e200\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;proportion of small conifer within 200 m, BRDLF\u003csub\u003e400\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;proportion of broadleaf trees within 400 m, RAILROAD\u0026thinsp;=\u0026thinsp;distance to nearest railroad (km), STRMDIST\u0026thinsp;=\u0026thinsp;distance to nearest stream (m), OWLFREQ\u0026thinsp;=\u0026thinsp;mean noise values within the range of 1.6-7.l kHz. ELEV\u0026thinsp;=\u0026thinsp;elevation (m), DATE\u0026thinsp;=\u0026thinsp;Julian date, LOWFREQNOISE\u0026thinsp;=\u0026thinsp;mean nightly noise values calculated from 250\u0026ndash;1000 Hz (dBFS), PRECIP\u0026thinsp;=\u0026thinsp;amount of daily precipitation (mm).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn addition to strong associations with covariates describing forest and topographical conditions, the top-ranked model of saw-whet owl landscape use (ψ) included a strong negative association with the level of sound within the high sensitivity range of saw-whet owl hearing (OWLFREQ; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Each standard deviation increase in OWLFREQ (2.25 dBFS), decreased the odds of landscape use by 26% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSaw-whet owl landscape use was also negatively associated with distance to railroad, such that each standard deviation increase in distance (11.55 km), decreased the odds of landscape use by 30% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Conversely, landscape use was positively associated with distance to stream, where an increase of 370 m (one standard deviation) increased the odds of landscape use by 46% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting that saw-whets may have been attracted to the railroad corridor but avoided areas near streams. Notably there was only one major railway in our study area, which travels through Cow Creek Canyon alongside a large creek (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSaw-whet owl landscape use was negatively associated with elevation (ELEV); where each standard deviation increase (198.63 m) decreased the odds of landscape use by 34.3% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Landscape use was negatively associated with proportion of broadleaf trees within a 400-m buffer (BRDLF\u003csub\u003e400\u003c/sub\u003e) and positively associated with proportion of small conifers within a 200-m buffer (SMCON\u003csub\u003e200\u003c/sub\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Each standard deviation increase in BRDLF\u003csub\u003e400\u003c/sub\u003e (0.07 m\u003csup\u003e2\u003c/sup\u003e/ha) decreased the odds of landscape use by 41%, while each standard deviation increase in SMCON\u003csub\u003e200\u003c/sub\u003e (0.20 m\u003csup\u003e2\u003c/sup\u003e/ha) increased the odds of landscape use by 45% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings provide evidence that variation in ambient noise (including geophony, biophony, and anthrophony) across the acoustic spectrum can influence the spatial ecology of an acoustically specialized predator. Saw-whet owl landscape use declined significantly with increasing sound levels within the 1.60\u0026ndash;7.10 kHz frequency bands, aligning with the species\u0026rsquo; peak auditory sensitivity \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This frequency-specific effect was not explained by structural habitat features alone, indicating that perceptual accessibility constrains landscape use.\u003c/p\u003e\u003cp\u003eThese results offer empirical support for the concept of the realized acoustic niche, a sensory-defined space within which organisms can successfully detect, process, and respond to ecologically meaningful cues \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Even in structurally suitable environments, species may be displaced when ambient sound conditions interfere with their perceptual abilities. We build on this framework by demonstrating how acoustic displacement arises when sound levels in species-relevant frequency bands mask key ecological information, reducing the functional extent of otherwise viable habitat \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eImportantly, the acoustic displacement effect we observed was not solely attributable to anthropogenic sources. Although many studies in soundscape ecology emphasize roads, aircraft, or urban noise, our study area featured complex acoustic inputs from natural and biological sources. Insects, amphibians, flowing water, and wind all contributed to the mid-frequency background noise measured in our study. Our results emphasize that ecological effects of masking are determined less by source category and more by spectral overlap and spatial-temporal consistency of the sound \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. This perspective recognizes that animals evolved under geophonic and biophonic acoustic pressures and that anthropogenic sounds may add to, rather than replace, existing background noise. In some cases, interactions among sources may have cumulative or non-additive effects on masking or behavioral response \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSaw-whet owls rely on passive detection of conspecific vocalizations and prey-generated cues, such as rustling in leaf litter. Their specialized auditory system includes asymmetric ears and fine frequency discrimination in the 1.60\u0026ndash;7.10 kHz range, with peak sensitivity near 4.00 kHz \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. These traits allow high foraging precision but increase susceptibility to masking in environments where sound levels are elevated in the same frequency range. In contrast to shifting signal amplitude or frequency to compensate for noise, passive-listening predators have fewer compensatory mechanisms \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Our finding that mid-frequency noise was a better predictor of landscape use than broadband low-frequency noise underscores that spectral specificity is essential in understanding sensory constraints on space use.\u003c/p\u003e\u003cp\u003eWhile increased distance to railroads had a small yet significant negative effect on landscape use, this variable may reflect a combination of acoustic and structural influences. Rail corridors can generate high-amplitude yet intermittent noise, but may also support edge habitats or small mammal populations that provide foraging opportunities \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The irregularity of this noise may allow periods of auditory recovery or detection, although it may also create unpredictable sensory environments that increase cognitive or energetic costs \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Future research could explore whether predictability, amplitude modulation, and periodicity of sound sources mediate the effects of masking across species and behaviors.\u003c/p\u003e\u003cp\u003eVegetation structure and topography influenced landscape use in expected directions. Areas with higher proportion of small conifers were more likely to be occupied, consistent with previous studies of saw-whet owl nesting and foraging preferences \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Elevation had a negative effect, possibly reflecting either prey availability or changing vegetation profiles. Broadleaf proportion was negatively associated with landscape use, which may be due to differences in sound attenuation or prey density in those habitats. Distance to streams had a negative effect, possibly reflecting increased noise levels or different vegetation profiles near riparian areas.\u003c/p\u003e\u003cp\u003eMethodologically, our study demonstrates the value of integrating passive acoustic monitoring with machine learning and species-specific sensory ecology. Automated classifiers such as PNW-Cnet v4 allow large-scale, repeatable detection of vocalizations for many species, enabling spatial replication at a level difficult to achieve through traditional field methods \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. When combined with frequency-targeted acoustic metrics and robust occupancy modeling frameworks, this approach facilitates direct testing of hypotheses about how soundscapes influence species distributions and richness \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. As passive acoustic monitoring tools become more accessible, opportunities to quantify perceptual constraints across taxa and ecosystems will expand.\u003c/p\u003e\u003cp\u003eThe concept of acoustic displacement applies not only to owls but to any species that relies on acoustic cues for ecological decision-making. This includes bats that echolocate in narrow frequency bands, frogs that use tonal mating calls, and marine mammals that depend on underwater sound for navigation and foraging \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In each case, the overlap between ambient noise and auditory sensitivity determines whether sensory information is lost or can be accessed. The generality of this mechanism suggests that sensory-based displacement may be widespread but underutilized in analyses of spatial ecology.\u003c/p\u003e\u003cp\u003eOur results also point to practical implications for conservation. Land managers and conservation planners often rely on structural habitat maps to identify suitable areas for protection or mitigation \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. However, if perceptual access is impaired, these maps may overestimate functional habitat. For acoustic specialists, it may be necessary to evaluate soundscape conditions within biologically relevant frequency bands as a criterion for habitat quality \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Conservation strategies might then include not only habitat restoration but also noise mitigation, timing of human activities, or establishment of acoustic refugia \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFuture work could extend these findings by quantifying the effect of acoustic masking to reproductive success, energy expenditure, and prey capture rates. Multi-season models incorporating survival or breeding data could help evaluate whether acoustic displacement has population-level impacts. Additionally, classification of noise sources and spectral structure at higher resolutions would allow researchers to disentangle the relative contributions of different soundscape components. Integrating spatial soundscape models with species-specific detections and behavioral data represents a promising frontier for landscape ecology and conservation science.\u003c/p\u003e\u003cp\u003eWe found rare evidence that ambient noise, including both natural and anthropogenic components, can influence a species\u0026rsquo; use of structurally suitable areas by interfering with its ability to perceive its environment. By demonstrating that sensory constraints shape the realized acoustic niche, we highlight the need to consider not just where animals can survive, but where they can hear. This perspective broadens our understanding of how species interact with dynamic soundscapes and offers a conceptual and methodological foundation for incorporating sensory ecology into spatial modeling, conservation planning, and biodiversity assessment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStudy Area\u003c/h2\u003e\u003cp\u003eWe collected audio data in spring and summer of 2021, within the saw-whet owl\u0026rsquo;s breeding season, across a 1,422-km\u003csup\u003e2\u003c/sup\u003e area in southwestern Oregon (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) as part of a broad scale PAM program designed for population monitoring of northern spotted owls (\u003cem\u003eStrix occidentalis caurina\u003c/em\u003e) \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This area contains mixed land ownership in a checkerboard of federally managed (U.S. Bureau of Land Management) and private lands. The area is part of the Klamath Mountain region, with a low-to-mid elevation mixed-conifer landscape used for natural resources, including logging, farming, and mining. The Klamath transportation network contains logging roads, secondary highways, and two major transportation networks: Interstate 5 and the Central Oregon and Pacific railroad line that travels through Cow Creek Canyon.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eWe used acoustic data collected from 276 monitoring stations within 69 randomly selected 5-km\u003csup\u003e2\u003c/sup\u003e hexagons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Approximately 20% of available hexagons (hexagons with \u0026ge;\u0026thinsp;25% federal ownership and \u0026ge;\u0026thinsp;50% forest capable) were randomly selected from a tessellation across the area. Each hexagon was sampled using four ARUs (SM4 Wildlife Acoustics, Maynard, MA, USA). Each ARU was deployed on mid to upper slopes, \u0026ge;\u0026thinsp;500 m from other ARUs, and \u0026ge;\u0026thinsp;50 m from roads, trails, and streams to reduce excessive noise. ARUs were deployed on small trees for 6\u0026ndash;8 weeks. We programmed ARUs to record for four hours during the crepuscular periods and ten minutes on-the-hour every remaining hour of the night and day \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The ARUs have the same sensitivity as human hearing, and their listening radius is impacted by factors limiting sound propagation, such as vegetation, terrain, wind, and rain. ARUs had a signal-to-noise ratio of 80 dB typical at 1 kHz, a sampling rate of 32,000 Hz, and a recording bandwidth of 20 Hz to 48 kHz at decibel levels of -33.5 dB to 122 dB.\u003c/p\u003e\n\u003ch3\u003eAudio Processing and Validation\u003c/h3\u003e\n\u003cp\u003eWe processed audio data using a convolutional neural network (PNW-Cnet v4) trained to identify various acoustic signals using 12-second spectrograms, including the territorial \u0026ldquo;toot\u0026rdquo; song of the saw-whet owl \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. To assess the performance of PNW-Cnet v4 for saw-whet owl signals relative to our study area, we randomly selected 200 audio clips with a PNW-Cnet classification score of \u0026ge;\u0026thinsp;0.95 for manual validation. We estimated classification precision as the number of manually confirmed saw-whet owl calls divided by the total reviewed.\u003c/p\u003e\u003cp\u003eWe used each calendar date (i.e., 24-hr period between 00:00 and 23:59) as survey occasions, up to 45 dates per recording station, omitting daytime audio (08:15 to 19:30) when saw-whet owls are unlikely to vocalize. We generated counts of daily apparent nighttime detections with a classification score of \u0026ge;\u0026thinsp;0.95 adjusted by our PNW-Cnet v4 precision and used a threshold of the 0.25 quantile of daily detections to assign survey presence to limit false positives. We manually confirmed presence/absence of saw-whet owl calls at all recording stations. If an occasion did not meet the threshold of apparent detections but had\u0026thinsp;\u0026ge;\u0026thinsp;1 manually confirmed detection(s), then we assigned a positive detection in that survey occasion. If we confirmed zero detections at a recording station, all survey occasions at the station were assigned non-detection, regardless of the number of PNW-Cnet v4 apparent detections.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSurvey covariates\u003c/h2\u003e\u003cp\u003eWe selected survey covariates which we predicted could affect sound attenuation or saw-whet owl calling behavior (Table S4). We used survey occasion Julian date (DATE) to examine if detection probability changed over the season. We hypothesized that high levels of nightly low-frequency noise would negatively impact our ability to detect owl calls. We calculated low-frequency noise levels from 250\u0026ndash;1000 Hz in decibels relative to full scale (dBFS) using Kaleidoscope Pro third-octave sound level analysis \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e to capture sound levels which may mask saw-whet owl calls (LOWFREQNOISE). We further hypothesized that weather conditions may affect owl calling behavior \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, thus we evaluated daily precipitation (PRECIP) and average daily temperature (TEMP)\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSite covariates\u003c/h3\u003e\n\u003cp\u003eWe calculated site-specific covariates within 200-, 400- and 600-m buffers around ARU locations to evaluate multi-scale variation in landscape use between stations (Table S4) \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. There is limited information about the home range size of non-migratory populations of saw-whet owls in the Pacific Northwest, so we based our buffer sizes on the likely listening radii of the recording units. We used ArcGIS Pro \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e and remotely sensed data to calculate landscape and terrain covariates (RUGGED, ELEV, TOPO). We used data from a gradient nearest neighbor dataset \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e to generate forest structure covariates (SNAG, STNDHGHT, CANCOV, OGSI80, BRDLF, SMCON; Table S4). Using data from the Oregon Department of Transportation, we calculated the distance from each recording station to the nearest railroad (RAILROAD). We also calculated the distance from each station to the closest unpaved road, secondary highway, and major highway (UNPAVEDRD, SCNDHWY, MJRHWY). We used data from the National Hydrography Dataset (Version 2.1) to nearest flowlines and non-ephemeral waters (STRMDIST).\u003c/p\u003e\u003cp\u003eWe hypothesized that loud noise within the most sensitive frequencies of the saw-whet owl\u0026rsquo;s hearing range would lead to decreased landscape use and that as the number of loud nights increased, landscape use would decrease. In addition to summarizing seasonal low-frequency noise levels (LOWFREQNOISE), we calculated noise covariates based on the most sensitive frequencies of the saw-whet owl\u0026rsquo;s hearing range \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. We ran third-octave sound level analyses \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e on frequencies between 1.6\u0026ndash;7.1 kHz (OWLFREQ) and their frequency of best sensitivity, 4 kHz (BESTHZ). We averaged sound level values across nightly audio files for each station. To further investigate if persistent \u0026lsquo;loud\u0026rsquo; nights within the low-frequency noise levels and the saw-whet owl\u0026rsquo;s sensitive hearing frequencies impacted landscape use \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, we summarized the proportion of survey occasions where noise levels were above the 25% percentile within both low frequencies and the most sensitive areas of the owls hearing range (LOW_LOUD, OWL_LOUD). We used the scale function in R to standardize covariates.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eWe fit single-season, single-species occupancy models in package RPresence \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e to examine saw-whet owl landscape use (ψ) while incorporating variation in daily nighttime detection probability (\u003cem\u003ep\u003c/em\u003e) \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. We used a secondary candidate-set strategy for model selection \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e and evaluated model support using Akaike\u0026rsquo;s Information Criterion corrected for small sample sizes (AICc). We developed sub-model sets independently, holding non-focal parameters constant while testing candidate covariates on the focal parameter, progressing to multivariate models using well supported covariates (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u0026ndash;S2). The final candidate model set included combinations of all parameter sub-models within 10 AICc units of the top-ranking sub-model that did not add uninformative parameters (Table S3) \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. We did not include correlated variables within sub-models (|r| \u0026gt;0.6; Table S5\u0026ndash;S6). We considered coefficient estimates (β) whose 95% confidence intervals (CI) did not include zero as supported. We used a Pearson chi-squared goodness-of-fit test with 1000 bootstraps to evaluate model fit and test for overdispersion \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eWe thank Oakridge Institute of Science and Education, U.S. Bureau of Land Management, U.S. Forest Service, and the Pacific Northwest Research Station for providing funding for this research. The findings and conclusions in this article are those of the authors and do not represent the views of the U.S. Forest Service or the U.S. Government. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the many field and lab technicians for their work gathering these data. Special thanks to A. Gasc, C. Gates, T. J. Mohler, C. R. Habib, L. R. Habib, H. Hester, K. E. Parker, M. Alsaid, and H. L. Basile for your support with noise analysis, maps, edits, and our many research questions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. R. Habib lead the formal analysis, conceptualization, investigation, and wrote the original draft and contributed to data curation, methodology, review and editing of the manuscript, software, validation, and visualization. N. M. Rugg and J. M. A. Jenkins contributed to conceptualization, data curation, formal analysis, methodology, software, validation, and review and editing of the manuscript. J. M. A. Jenkins, N. M. Rugg, and D. B. Lesmeister supervised the project and analysis. G. Nu\u0026ntilde;ez-Alvarez contributed to reviewing and editing of the manuscript and visualization. D. B. Lesmeister supported conceptualization and methodology and provided funding acquisition, project administration, resources, and review and editing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo the best of our knowledge, the named authors have no conflict of interest, financial or otherwise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe full dataset necessary to run these analyses are publicly available on Zenodo through the following DOI: \u003cstrong\u003e10.5281/zenodo.16944516\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePijanowski, B. C. et al. 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Uninformative Parameters and Model Selection Using Akaike\u0026rsquo;s Information Criterion. \u003cem\u003eJ. Wildl. Manage.\u003c/em\u003e \u003cb\u003e74\u003c/b\u003e, 1175\u0026ndash;1178 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHabib, A. Figure 1.Created in BioRender. (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://BioRender.com/t9xixd9\u003c/span\u003e\u003cspan address=\"https://BioRender.com/t9xixd9\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anthropogenic noise, sensory ecology, occupancy modeling, passive acoustic monitoring, northern saw-whet owl, acoustic displacement","lastPublishedDoi":"10.21203/rs.3.rs-7483314/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7483314/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmbient acoustic conditions shape how animals perceive and interact with their environments, yet their role in structuring space use remains underexplored. Noise can mask biologically informative sounds which can impact foraging success, physiological fitness, and displace animals from viable habitat. Here, we test how landscape variables and soundscape characteristics across a mixed-use forest landscape affect spatial distribution of a nocturnal predator with specialized hearing. We used passive acoustic recordings to assess the effects of noise levels within biologically relevant frequency ranges on landscape use of the northern saw-whet owl (\u003cem\u003eAegolius acadicus\u003c/em\u003e) across 276 sites in Oregon, USA. Owl landscape use declined with increasing noise levels in the 1.60\u0026ndash;7.10 kHz band, corresponding with species peak auditory sensitivity. In contrast, general low-frequency sound (0.25-1.00 kHz) was a poor predictor of landscape use but negatively affected nightly detection probability. These results provide evidence that sensory masking from ambient soundscapes can constrain the realized acoustic niche and drive acoustic displacement. Our findings highlight the importance of considering full-spectrum acoustic environments in spatial ecology and suggest that species distributions are shaped not only by physical habitat but also by the perceptual accessibility of ecological information.\u003c/p\u003e","manuscriptTitle":"Sensory interference displaces an acoustically specialized predator","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 18:15:41","doi":"10.21203/rs.3.rs-7483314/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-31T12:08:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T10:07:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-23T16:22:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T15:32:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314225488209816937143280877444271828957","date":"2025-10-02T13:19:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90607195826002182831813526904710306452","date":"2025-10-02T12:39:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136139871522325141523439607271803141378","date":"2025-10-01T20:20:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94569877060072378302245665046936402767","date":"2025-09-08T18:45:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-08T15:28:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-08T15:26:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-05T15:43:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-02T22:47:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-02T22:43:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"06966de8-a332-4405-abbb-91fddf4b8f5c","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54483577,"name":"Biological sciences/Ecology"},{"id":54483578,"name":"Earth and environmental sciences/Ecology"},{"id":54483579,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2025-12-08T16:06:51+00:00","versionOfRecord":{"articleIdentity":"rs-7483314","link":"https://doi.org/10.1038/s41598-025-30437-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-04 15:58:11","publishedOnDateReadable":"December 4th, 2025"},"versionCreatedAt":"2025-09-15 18:15:41","video":"","vorDoi":"10.1038/s41598-025-30437-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-30437-z","workflowStages":[]},"version":"v1","identity":"rs-7483314","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7483314","identity":"rs-7483314","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}