DOI: 10.5061/dryad.xpnvx0kn6
title: Time series methods for the analysis of soundscapes and other cyclical ecological data

description: 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.
description: 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.
description: # Time series methods for the analysis of soundscapes and other cyclical
 ecological data
 [https://doi.org/10.5061/dryad.xpnvx0kn6](https://doi.org/10.5061/dryad.xpnvx0kn6) Example dataset used for demonstrating the methods in "Times Series Methods for the Analysis of Soundscapes and Other Cyclical Ecological Data" Methods in Ecology and Evolution. This data represents soundscape data for eight tropical forest sites in Gabon collected between February and July 2021. See methods for more information. The soundscape is characterized using the soundscape index Power Minus Noise (PMN) for 256 frequency bins between 0-11 kHz for each minute of the day. PMN is a proxy for acoustic activity and provides a relatively simple index to demonstrate our methodological approach. ## Description of the data and file structure Column names: * Site: Site code (factor; 8 levels) * DatetimeFinal: Date time information (datatime object) * TotPMN: Values for characterizing the soundscape, Power minus noise (numeric) * Season: Seasonal information for when the data was collected (factor; 2 levels) ## Code/Software Accompanying code is available as an R notebook or R markdown file. The code includes the following stages: * Importing and visually inspecting the PMN data * Plotting the data at different time resolutions * Computing the periodogram to analyze the periodic components of the time series * Visualizing the waveforms * Magnitude squared coherence * Multitaper principal component analysis See code for the required packages and corresponding functions. ## Data and code ownership Data was collected by Zuzana Burivalova (University of Wisconsin-Madison) and the corresponding code was authored by Charlotte L. Haley (Argonne National Laboratory).