Time series methods for the analysis of soundscapes and other cyclical ecological data

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.