DOI: 10.5061/dryad.k6djh9w61
title: A meta-analysis of the influence of anthropogenic noise on terrestrial wildlife communication strategies

description: 1. Human-caused noise pollution dominates the soundscape of modern
 ecosystems, from urban centers to national parks. Though wildlife can
 generally alter their communication to accommodate many types of natural
 noise (e.g. wind, wave action, heterospecific communication), noise
 pollution from anthropogenic sources pushes the limits of wildlife
 communication flexibility by causing loud, low-pitched, and
 near-continuous interference. Because responses to noise pollution are
 variable and taxa-specific, multi-species risk assessments and mitigation
 are not currently possible. 2. We conducted a meta-analysis to synthesize
 noise pollution effects on terrestrial wildlife communication.
 Specifically, we assessed: 1) the impacts of noise pollution on modulation
 of call rate, duration, amplitude, and frequency (including peak, minimum,
 and maximum frequency); and 2) the literature on anthropogenic noise
 pollution by region, taxa, study design, and disturbance type. 3.
 Terrestrial wildlife (results driven by avian studies) generally respond
 to noise pollution by calling with higher minimum frequencies, while they
 generally do not alter the amplitude, maximum frequency, peak frequency,
 duration, and rate of calling. 4. The literature on noise pollution
 research is biased towards birds, population-level studies, urban noise
 sources, and study systems in North America. 5. Policy applications Our
 study reveals the ways in which wildlife can alter their signals to
 contend with anthropogenic noise, and discusses the potential fitness and
 management consequences of these signal alterations. This information,
 combined with an identification of current research needs, will allow
 researchers and managers to better develop noise pollution risk assessment
 protocols and prioritize mitigation efforts to reduce anthropogenic
 noise.12-Mar-2021
description: <i>Literature Search Strategy and Inclusion
 Criteria</i>             We searched the
 peer-reviewed scientific literature to synthesize information regarding
 noise pollution impacts on wildlife acoustic communication and to assess
 research gaps and biases. We restricted the search to terrestrial systems
 because general approaches to noise pollution risk assessment and
 recommendations for noise mitigation already exist for some coastal and
 marine systems (Southall et al. 2007). Perhaps more importantly, a vast
 body of research conducted to date on marine wildlife has yielded valuable
 knowledge such as species-specific spectral sensitivity, critical impact
 thresholds, and mitigation effectiveness which can be drawn upon to
 advance general theory and research and to develop further regulatory
 guidelines (Erbe et al. 2016). Finally, the physics of sound transmission
 differ between water and air, affecting both how sound is perceived by
 organisms and potential mitigation strategies (Würsig et al. 2000, Shannon
 et al. 2015). We used Web of Science (search conducted 4/5/2018) to search
 for studies investigating the impact of noise pollution on wildlife
 modulation of call frequency, rate, duration, and amplitude (see Table 2
 for specific search terms). We assessed these multiple communication
 response variables even though they may be related because each response
 may have different ecological and/or evolutionary implications. An initial
 search produced 815 studies. After implementing all inclusion criteria
 (see below), our search resulted in 181 data points from 32 studies
 representing six continents (Table 3). We used the “Analyze Results”
 feature in Web of Science to filter out irrelevant disciplines (e.g.,
 Audiology, Speech Pathology, n<sub>excluded</sub> = 347).
 After compiling remaining results into a database, we removed duplicate
 studies (n<sub>excluded</sub> = 5) and studies determined to
 be topically irrelevant based on reading of all titles
 (n<sub>excluded</sub> = 117). We excluded studies broadcasting
 white noise as a treatment, as we were interested in responses to spectral
 characteristics that more closely match environmental noise pollution
 (i.e., loud, low-frequency sounds, n<sub>excluded</sub> = 3).
 However, we retained one study that explicitly manipulated the
 characteristics of white noise to approximate low-frequency traffic
 sounds. We excluded studies conducted in a laboratory setting, as we were
 only interested in responses of free-living wildlife to noises experienced
 in their natural habitat (n<sub>excluded</sub> = 5). After
 detailed screening of article texts, we removed studies that did not
 assess effects of noise pollution on the above focal response variables
 and studies with analysis methods or reporting that precluded us from
 extracting a relevant effect size (n<sub>excluded</sub> =
 59).             For remaining studies, we extracted
 the location, focal taxa, response variable, sound source, and study
 design. We also extracted means, sample sizes, and standard deviations of
 response variables for studies assessing categorical predictor variables
 (e.g., call characteristics at quiet and noisy sites), or values of
 Pearson’s r for studies assessing continuous predictor variables (e.g.,
 response characteristics over a gradient of decibel levels). In studies
 with multiple treatments, we used the two extreme ends of the
 environmental sound spectrum for analysis. For example, if a study tested
 call rates in “quiet”, “moderate”, and “loud” environments, we compared
 responses between “quiet” and “loud” sites. Sound sources included
 airplane (n = 2), construction (n = 6), energy development (n = 17),
 roadway (n = 52), urban (n = 101), and white noise (n = 3). We also
 distinguished study designs as event-based (n = 41) versus continuous (n =
 140). Event-based study designs evaluated instantaneous signal flexibility
 in the presence of anthropogenic sound (e.g., a grasshopper calling more
 loudly during an airplane overflight compared to normal conditions, Fig.
 2). Continuous study designs, on the other hand, evaluated differences in
 acoustic properties between populations in loud and quiet environments
 (e.g., communication characteristics of red-winged blackbirds,
 (<i>Agelaius phoenicus), </i>in rural versus urban
 environments; Fig. 2). Following our literature search, we incorporated a
 specific search for bat studies, as they were underrepresented in our
 initial search and we felt that they are good models for the study of
 anthropogenic sound impacts due to their reliance on acoustic information
 for both communication and foraging.  
 <i>Analysis</i> To assess
 potential biases in the noise pollution literature, we assessed observed
 versus expected proportions of studies using Pearson’s
 χ<sup>2</sup> tests. We conducted these tests to analyze
 numbers of studies for each response variable, sound source, focal taxa,
 continent, and study design; in each case we tested a null hypothesis that
 an equal proportion of studies have been conducted for each category
 (e.g., 50% of studies each for event-based and continuous study designs).
 To control the Type I error rate, we employed a Holm’s Sequential
 Bonferroni correction. We conducted a meta-analysis to
 assess wildlife responses to noise pollution using the metafor package
 (Viechtbauer, 2010) in the R statistical environment (version 3.4.1, R
 Core Team 2017). We ran mixed-effects meta regression models with study
 design (event-based versus continuous), and taxa as fixed effects and
 study ID as a random effect. When possible, we calculated
 Hedge’s <i>g</i> for each study that used a categorical noise
 treatment. When studies evaluated responses to noise along a continuous
 gradient, we calculated Hedge’s <i>g</i> using Pearson’s
 <i>r</i>. To evaluate overall effect of each response variable
 (Minimum Frequency, Maximum Frequency, Peak Frequency, Duration, Rate, and
 Amplitude), as well as the effect of study type and taxa, we evaluated
 overlap of 95% confidence intervals with zero. After conducting analyses,
 we constructed Q-Q plots to visually assess model fit.