Uncovering Spatial Variation in Acoustic Environments Using Sound Mapping

Abstract

Animals select and use habitats based on environmental features relevant to their ecology and behavior. For animals that use acoustic communication, the sound environment itself may be a critical feature, yet acoustic characteristics are not commonly measured when describing habitats and as a result, how habitats vary acoustically over space and time is poorly known. Such considerations are timely, given worldwide increases in anthropogenic noise combined with rapidly accumulating evidence that noise hampers the ability of animals to detect and interpret natural sounds. Here, we used microphone arrays to record the sound environment in three terrestrial habitats (forest, prairie, and urban) under ambient conditions and during experimental noise introductions. We mapped sound pressure levels (SPLs) over spatial scales relevant to diverse taxa to explore spatial variation in acoustic habitats and to evaluate the number of microphones needed within arrays to capture this variation under both ambient and noisy conditions. Even at small spatial scales and over relatively short time spans, SPLs varied considerably, especially in forest and urban habitats, suggesting that quantifying and mapping acoustic features could improve habitat descriptions. Subset maps based on input from 4, 8, 12 and 16 microphones differed slightly (< 2 dBA/pixel) from those based on full arrays of 24 microphones under ambient conditions across habitats. Map differences were more pronounced with noise introductions, particularly in forests; maps made from only 4-microphones differed more (> 4 dBA/pixel) from full maps than the remaining subset maps, but maps with input from eight microphones resulted in smaller differences. Thus, acoustic environments varied over small spatial scales and variation could be mapped with input from 4–8 microphones. Mapping sound in different environments will improve understanding of acoustic environments and allow us to explore the influence of spatial variation in sound on animal ecology and behavior.

Landscape influence on biophony in an urban environment in the European Alps

“…While we observed that traffic noise was a dominate component at many of our recording locations, other foreground and background sources were also present. As demonstrated by Job et al (2016), urban acoustic environments can be highly variable at small scales (i.e. street scales), although global trends have also been found on large scales (i.e.…”

GIS-based modeling of tornado siren sound propagation: Refining spatial extent and coverage estimations

“…Future work on this application with SPreAD-GIS might benefit from altering the tool to place less importance on the wind factor (and adjust other factors as necessary). Future work might consider ground-truthing sound estimates during monthly siren tests using in situ microphone arrays similar to Job et al (2016). This would confirm or refute the importance of wind (and other factors), which in this example was highly significant, on the sound propagation estimates.…”

“…While this type of approach is efficient, it is not without its limits. Ultimately, the analysis is constrained by the fact that buffers are based on ideal, theoretical estimates of the distance from the siren that the siren noise will reach; these types of estimates do not factor in the intricacies of sound propagation (Webster, 2014) and the role of environmental factors such as topography, weather conditions (e.g., temperature, wind, humidity), and land cover (Job et al, 2016). Such factors influence citizens' ability to hear the sirens; e.g., Hodler (1982) observed that sirens were inaudible to Kalamazoo residents located upwind.…”

“…In this way, sound propagation modeling to date has focused on ecological applications (i.e. soundscape ecology; Barber et al, 2010Barber et al, , 2011Job et al, 2016). Research applications in soundscape ecology led to the development of the GIS-based 'System for the Prediction of Acoustic Detectability' toolset or SPreAD-GIS (Reed et al, 2010(Reed et al, , 2012(Reed et al, , 2016.…”

Artificial night light and anthropogenic noise interact to influence bird abundance over a continental scale

“…Furthermore, environmental features can affect the propagation and intensity of light and noise exposure. For instance, a study that mapped sound propagation from playbacks in three terrestrial habitats found forests had broader sound pressure level gradients than prairie or urban habitats due to more sound reflection and reverberation (Job et al, 2016). Seasonally changing environmental conditions could also alter an organism's response to sensory stimuli.…”