Biogeographical and analytical implications of temporal variability in geographically diverse soundscapes

• Published in: Ecological indicators 2021-02, Vol.121, p.106794, Article 106794
• Main Authors: Francomano, Dante, Gottesman, Benjamin L., Pijanowski, Bryan C.
• Format: Article
• Language: English
• Subjects: Acoustic monitoring; Ecoacoustics; Soundscape ecology; Study design; Temporal variability
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2020.106794

•Continuous soundscape recordings were made in eight ecosystems on four continents.•Temporal variability of soundscapes was quantified using two distinct measures.•Daytime exhibited high temporal variability relative to dusk and nighttime.•Increasing evenness of subsampling distribution reduces error in representation.•Marginal precision gains diminish substantially beyond 1/3 recording time. Unprecedented rates of biodiversity loss and intensifying human attempts to rectify the biodiversity crisis have heightened the need for standardized, large-scale, long-duration biodiversity monitoring at fine temporal resolution. While some innovative technologies such as passive acoustic monitoring are well suited for such monitoring challenges, many questions remain as to how they should be scaled out and optimally implemented across ecosystems. Our research questions center on temporal sampling regimes—how frequently and how long one should collect data to represent biodiversity conditions over a given timeframe. Addressing this concern in the context of passive acoustic monitoring, we investigated whether temporal soundscape variability—the characteristic short-term acoustic change in an environment—is consistent across ecosystems and times of day, and we considered how various temporal subsampling schemes affect the representativeness of resultant acoustic index values, relative to continuous sampling. We quantified soundscape variability at eight sites across four continents based on temporal autocorrelation ranges and standard deviations of acoustic index values, and we created a heuristic model to classify types of soundscape variability based on those two variables. Drawing on values derived from three distinct acoustic indices, we found that the characteristic temporal variability of soundscapes varied between sites and times of day (dawn, daytime, dusk, and nighttime). Some sites exhibited little difference in variability between times of day whereas other sites exhibited greater within-site differences between times of day than many inter-site differences. Daytime soundscapes generally tended to exhibit more temporal variability than nighttime soundscapes. We also compared potential subsampling schemes that could be advantageous in terms of power, data storage, and data analysis costs by modeling subsample error as a function of total analysis time and number of subsamples within a larger block of time. Greater numbers of evenly distributed subdivisions d