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The way in which terrestrial organisms use the acoustic realm is fundamentally important and shapes behavior, populations, and communities, but how background acoustics, or noise, influence the patterns and processes in ecology is still relatively understudied. In this review, we summarize how background acoustics have traditionally been studied from the signaling perspective, discuss what is known from a receiver's perspective, and explore what is known about population- and community-level responses to noise. We suggest that there are major gaps linking animal physiology and behavior to fitness; that there is a limited understanding of variation in hearing within and across species, especially in the context of real-world acoustic conditions; and that many puzzling responses to noise could be clarified with a community-level lens that considers indirect effects. Failing to consider variation in acoustic conditions, and the many ways organisms use and interact via this environmental dimension, risks a limited understanding of natural systems.

Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 54 is November 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

 

Traffic noise is one of the leading causes of reductions in animal abundances near roads. Acoustic masking of conspecific signals and adventitious cues is one mechanism that likely causes animals to abandon loud areas. However, masking effects can be difficult to document in situ and the effects of infrequent noise events may be impractical to study. Here, we present the Soundscapes model, a stochastic individual-based model that dynamically models the listening areas of animals searching for acoustic resources (“searchers). The model also studies the masking effects of noise for human detections of the searchers. The model is set in a landscape adjacent to a road. Noise produced by vehicles traveling on that road is represented by calibrated spectra that vary with speed. Noise propagation is implemented using ISO-9613 procedures. We present demonstration simulations that quantify declines in searcher efficiency and human detection of searchers at relatively low traffic volumes, fewer than 50 vehicles per hour. Traffic noise is pervasive, and the Soundscapes model offers an extensible tool to study the effects of noise on bioacoustics monitoring, point-count surveys, the restorative value of natural soundscapes, and auditory performance in an ecological context.

 

Noise pollution can affect species' behaviours and distributions and may hold significant consequences for natural communities. While several studies have researched short-term effects of noise, no long-term research has examined whether observed patterns persist or if community recovery can occur. We used a long-term study system in New Mexico to examine the effects of continuous natural gas well noise exposure on seedling recruitment of foundational tree species ( Pinus edulis , Juniperus osteosperma ) and vegetation diversity. First, we examined seedling recruitment and vegetation diversity at plots where current noise levels have persisted for greater than 15 years. We then examined recruitment and diversity on plots where noise sources were recently removed or added. We found support for long-term negative effects of noise on tree seedling recruitment, evenness of woody plants and increasingly dissimilar vegetation communities with differences in noise levels. Furthermore, seedling recruitment and plant community composition did not recover following noise removal, possibly due in part to a lag in recovery among animals that disperse and pollinate plants. Our results add to the limited evidence that noise has cascading ecological effects. Moreover, these effects may be long lasting and noise removal may not lead to immediate recovery. 

A major challenge for studies assessing drivers of phenotypic divergence is the statistical comparison of taxa with unique, often unknown, evolutionary histories, and for which there are no clear expected trait values. Because many traits are fundamentally constrained by energy availability, we suggest that trait values predicted by scaling theories such as the metabolic theory of ecology (MTE) can provide baseline expectations. Here, we introduce a metabolic scaling‐based approach to test theory involving the direction and magnitude of ecological and sexual selection, using vocal frequency as an example target of selection. First, we demonstrate that MTE predicts the relationship between the natural log of body size and natural log of vocal frequency across 795 bird species, controlling for phylogeny. Family‐wide deviations in slope and intercepts from MTE estimates reveal taxa with potentially important differences in physiology or natural history. Further, species‐level frequency deviations from MTE expectations are predicted by factors related to ecological and sexual selection and, in some cases, provide evidence that differs from current understanding of the direction of selection and identity of ecological selective agents. For example, our approach lends additional support to the findings from many cross‐habitat studies that suggest that dense vegetation selects for lower frequency signals. However, our analysis also suggests that birds in non‐forested environments vocalize at frequencies higher than expected based on MTE, prompting intriguing questions about the selective forces in non‐forest environments that may act on vocal frequency. Additionally, vocal frequency deviates more strongly from MTE expectations among species with smaller repertoires and those with low levels of sexual dichromatism, complicating the use of these common sexual selection surrogates. Broad application of our metabolic scaling approach might provide an important complementary approach to understanding how selection shapes phenotypic evolution by offering a common baseline across studies and taxa and providing the basis to explore evolutionary trade‐offs within and among multicomponent and multimodal traits.