Ecoacoustics in the rain: understanding acoustic indices under the most common geophonic source in tropical rainforests

Abstract

Rainfall is one of the most predominant geophonic sources in nature, and the major climatic phenomenon influencing species biology in tropical ecosystems. Although its effects on acoustic indices have been studied, rainfall is recognized as a nuisance factor affecting their estimation. Consequently, files with rainfall sounds are typically removed from ecoacoustic analyses. In tropical rainforests, where rainfall is a common and unpredictable event, its influence on acoustic indices needs to be explicitly examined before implementing acoustic passive monitoring. Using mixed‐effects models we assessed the effect of different rainfall conditions on the direction and magnitude of the values of eight commonly used acoustic indices. We obtained 18336 1‐min recordings from 28 sampling sites in a montane forest on the northern Andes of Colombia between May‐July 2018. We identified 2867 1‐min recordings containing light to heavy rainfall. We found that both rainfall occurrence and its variation in intensity were associated with increases in ACI, ADI, H, and M index values, and decreases in AEI, BI, NDSI, and NP values. The estimated indices exhibited differential sensitivity to rainfall, with M, NDSI, and NP showing higher differences associated with increasing frequency and intensity of rainfall. Regardless the direction of change in index values caused by rainfall, we found that the magnitude of variation depended on the index. For instance, ACI and BI indices showed low sensitivity and can be considered as reliable acoustic metrics, even during heavy intensity rainfall. In contrast, M, NDSI, and NP might lead to misleading inferences, if rainfall events are not considered during calculation. We stress the importance of careful interpretation of biological inferences based on these sensitive indices and encourage an explicit assessment of rainfall, particularly in short‐term acoustic surveys in highly pluvious regions where rainfall is a conspicuous component of the soundscape.

Automated classification of avian vocal activity using acoustic indices in regional and heterogeneous datasets

“…This approach has successfully been used to characterize daily diel patterns (Bradfer‐Lawrence et al., 2019; Burivalova et al., 2017; Phillips et al., 2018), seasonal phenology (Buxton et al., 2016; Phillips et al., 2018) and vocal activity patterns (Bradfer‐Lawrence et al., 2020; Oliver et al., 2018) in birds and other acoustically active species. Acoustic data can be screened to identify biotic activity from geophony or anthrophony prior to manual processing to increase efficiency (Metcalf et al., 2020; Sanchez‐Giraldo et al., 2020). Automated classification could also be used to investigate patterns in seasonal phenology at larger scales, using pre‐existing ARU datasets or by integrating multiple datasets as we have done here.…”

“…Several methods for calculating acoustic indices are readily available (Sueur, Aubin & Simonis, 2008; Sueur, Pavoine, et al., 2008; Towsey et al., 2018; Villanueva‐Rivera & Pijanowski, 2018) and existing studies use a variety of audio parameters such as different combinations of acoustic indices and types of audio recording units (Oliver et al., 2018; Phillips et al., 2018). Acoustic indices are sensitive to changes in recording parameters (Bradfer‐Lawrence et al., 2019), weather conditions (Farina et al., 2011; Sanchez‐Giraldo et al., 2020) and background or anthropogenic noise (Fairbrass et al., 2017). How this influences classification performance of audio recordings and soundscape interpretation is not well understood.…”

“…How this influences classification performance of audio recordings and soundscape interpretation is not well understood. For example, indicators such as the Acoustic Complexity Index (ACI) which are often used to measure diversity can also be positively correlated to rain and weather events in some studies (Bradfer‐Lawrence et al., 2019), but have shown to have relatively little effect in others (Sanchez‐Giraldo et al., 2020). Extensive investigation and validation of the relationship between individual indices and ecological community data are important for the effective use of acoustic indices (Harris et al., 2016) and often requires project‐specific calibration (Sueur, Pavoine, et al., 2008).…”

Ecoacoustics: acoustic sensing for biodiversity monitoring at scale

“…One paradigm measures the acoustic diversity of a soundscape through the computation of acoustic indices: algorithmically straightforward and highly scalable, these indices yield evidence of biodiversity that is implicit, but holistic across many taxa. Sánchez‐Giraldo et al (2020) and Roca and Van Opzeeland (2019) conduct large‐scale studies in very different ecological contexts – respectively in forests in the Columbian Andes, and underwater in the Southern Ocean – and quantify the reliability of such indices. Sánchez‐Giraldo et al (2020) tackle the widely encountered issue of the effect of rain noise on index computation, while Roca and Van Opzeeland (2019) reveal acoustic significant differences between distinct Antarctic marine habitats using a set of indices.…”

“…Sánchez‐Giraldo et al (2020) and Roca and Van Opzeeland (2019) conduct large‐scale studies in very different ecological contexts – respectively in forests in the Columbian Andes, and underwater in the Southern Ocean – and quantify the reliability of such indices. Sánchez‐Giraldo et al (2020) tackle the widely encountered issue of the effect of rain noise on index computation, while Roca and Van Opzeeland (2019) reveal acoustic significant differences between distinct Antarctic marine habitats using a set of indices. Campos‐Cerqueira et al (2019) develop another type of index by extracting compressed data from a long‐term spectrogram representation.…”