Biodiversity monitoring has entered an era of ‘big data’, exemplified by a near-continuous collection of sounds, images, chemical and other signals from organisms in diverse ecosystems. Such data streams have the potential to help identify new threats, assess the effectiveness of conservation interventions, as well as generate new ecological insights. However, appropriate analytical methods are often still missing, particularly with respect to characterizing cyclical temporal patterns. Here, we present a framework for characterizing and analysing ecological responses that represent nonstationary, complex temporal patterns and demonstrate the value of using Fourier transforms to decorrelate continuous data points. In our example, we use a framework based on three approaches (spectral analysis, magnitude squared coherence, and principal component analysis) to characterize differences in tropical forest soundscapes within and across sites and seasons in Gabon. By reconstructing the underlying, cyclic behaviour of the soundscape for each site, we show how one can identify circadian patterns in acoustic activity. Soundscapes in the dry season had a complex diel cycle, requiring multiple harmonics to represent daily variation, while in the wet season there was less variance attributable to the daily cyclic patterns. Our framework can be applied to most continuous, or near-continuous ecological data collected at a fine temporal resolution, allowing ecologists to explore patterns of temporal autocorrelation at multiple levels for biologically meaningful trends. Such methods will become indispensable as biological big data are used to understand the impact of anthropogenic pressures on biodiversity and to inform efforts to mitigate them.

We used acoustic data collected from eight sites in the Ogooué-Ivindo province of Gabon to demonstrate how time-series approaches can be leveraged to compare cyclical trends within and between groups of sites. All soundscape sampling occurred in closed, Gabonese rainforest with minimal habitat disturbance for at least twenty years. First, we sampled the soundscape in the rainy season at four sites within Ivindo National Park, between February 19th and March 2nd 2021 (referred to as the Ivindo sites). Second, we sampled the soundscape in the dry season at four sites near Massaha between July 17th and July 23rd 2021 (hereafter referred to as the Massaha sites, about 15km from the Ivindo sites). At the time of sampling, the Massaha sites were located within a logging concession but no logging activity had commenced and there was an ongoing petition for the area to be re-designated as a community conservation area. Additionally, we used one site from the Lope National Park.

At each site, we deployed one bioacoustic recorder to quantify the soundscape, separating each sampling site by at least 1 km to ensure independence. Sample points were also positioned at least 200 m from roads, trails, and rivers. At each site, we deployed one Bioacoustic Recorder (BAR-LT, Frontier Labs) at 1.8 m above ground, with a single omnidirectional microphone pointing down. Recorders were programmed for continuous, autonomous recording in 30-minute segments for at least six days and set to record at 40 dB gain and a sample rate of 44.1 kHz. Incomplete days, e.g. the day of deployment and collection, were excluded from the analysis, to prevent the inclusion of human sounds and disturbance to the soundscape. 

To characterize the soundscape, we calculated the soundscape index Power Minus Noise (PMN) for 256 frequency bins between 0-11 kHz (~43 Hz bandwidth each) and for each minute of the day, using `AnalysisPrograms.exe'. PMN is the difference between the maximum decibel of each frequency bin and the corresponding decibel of the background noise profile for that bin. Therefore, it provides a measure of the sound intensity for each frequency bin absent of background noise and provides a proxy for acoustic activity. For further analyses, we summed all 256 PMN values for each minute of the day, yielding 1440 data points per day per site. We chose the PMN index as an example index in our time series analyses, because of its relatively simple interpretation and statistical properties.