Evaluating acoustic indices in the Valdivian rainforest, a biodiversity hotspot in South America

Moreno‐Gómez, Felipe N.; Bartheld, José Luis; Silva-Escobar, Andrés A.; Briones, Raúl; Márquez, Rafael; Penna, Mario

doi:10.1016/j.ecolind.2019.03.024

Cited by 33 publications

(16 citation statements)

References 41 publications

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“…It measures the disparity between the quietest and loudest 1 kHz frequency bins. Again, the BI has been found to be a good predictor of diversity in some studies (Eldridge et al., 2018; Gasc et al, 2017; Hilje et al, 2017; Mitchell et al, 2020) while others have found it to be poor (Fuller et al., 2015; Moreno‐Gómez et al, 2019), although concerns about the limitations of the methodologies used in these studies apply here too. We expect both indices to increase with increasing species richness and species abundance, and for correlations between both abundance and richness with the indices to be strongest in the frequency and time bins that are most dominated by the target taxa (Table 2), particularly diurnal bird species at dawn between 0.3 and 12 kHz and nocturnal taxa at night between 0.3 and 4 kHz.…”

Section: Methodsmentioning

confidence: 78%

“…Acoustic Complexity Index measures the irregularity in amplitude across time samples by frequency bin, relative to the total amplitude of the frequency bin. The Acoustic Complexity Index has been found to significantly correlate with species richness for some taxa (Bertucci et al, 2016; Bradfer‐Lawrence et al., 2020; Eldridge et al., 2018; Mitchell et al, 2020), while in others it showed little or no correlation (Fuller et al., 2015; Mammides et al., 2017; Moreno‐Gómez et al, 2019) although this may be due to limitations in methodology and small sample sizes. In contrast, the BI is generally applied to narrower frequency bins, and is intended to provide relative abundance of avian community within a frequency range that contains most bird sound (Boelman et al., 2007).…”

Section: Methodsmentioning

confidence: 99%

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Acoustic indices perform better when applied at ecologically meaningful time and frequency scales

et al. 2020

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Acoustic indices are increasingly employed in the analysis of soundscapes to ascertain biodiversity value. However, conflicting results and lack of consensus on best practices for their usage has hindered their application in conservation and land‐use management contexts. Here we propose that the sensitivity of acoustic indices to ecological change and fidelity of acoustic indices to ecological communities are negatively impacted by signal masking. Signal masking can occur when acoustic responses of taxa sensitive to the effect of interest are masked by less‐sensitive acoustic groups, or target taxa sonification is masked by non‐target noise. We argue that by calculating acoustic indices at ecologically appropriate time and frequency bins, masking effects can be reduced and the efficacy of indices increased. We test this on a large acoustic dataset collected in Eastern Amazonia spanning a disturbance gradient of undisturbed, logged, burned, logged‐and‐burned and secondary forests. We calculated values for two acoustic indices: the Acoustic Complexity Index and the Bioacoustic Index, across the entire frequency spectrum (0–22.1 kHz), and four narrower subsets of the frequency spectrum; at dawn, day, dusk and night. We show that signal masking has a large impact on the sensitivity of acoustic indices to forest disturbance classes. Calculating acoustic indices at a range of narrower time–frequency bins substantially increases the classification accuracy of forest classes by random forest models. Furthermore, signal masking led to misleading correlations, including spurious inverse correlations, between biodiversity indicator metrics and acoustic index values compared to correlations derived from manual sampling of the audio data. Consequently, we recommend that acoustic indices are calculated either at a range of time and frequency bins, or at a single narrow bin, predetermined by a priori ecological understanding of the soundscape.

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Section: Methodsmentioning

confidence: 78%

Section: Methodsmentioning

confidence: 99%

Acoustic indices perform better when applied at ecologically meaningful time and frequency scales

et al. 2020

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“…territorial behavior, spawning aggregations, migratory patterns) with applications across a wide range of terrestrial (e.g. woodland forest, desert) [ 52 – 54 ] and aquatic (e.g. coral reefs, oyster reefs, seagrass beds, kelp forest) [ 55 – 59 ] ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Hurricane impacts on a coral reef soundscape

2021

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Soundscape ecology is an emerging field in both terrestrial and aquatic ecosystems, and provides a powerful approach for assessing habitat quality and the ecological response of sound-producing species to natural and anthropogenic perturbations. Little is known of how underwater soundscapes respond during and after severe episodic disturbances, such as hurricanes. This study addresses the impacts of Hurricane Irma on the coral reef soundscape at two spur-and-groove fore-reef sites within the Florida Keys USA, using passive acoustic data collected before and during the storm at Western Dry Rocks (WDR) and before, during and after the storm at Eastern Sambo (ESB). As the storm passed, the cumulative acoustic exposure near the seabed at these sites was comparable to a small vessel operating continuously overhead for 1–2 weeks. Before the storm, sound pressure levels (SPLs) showed a distinct pattern of low frequency diel variation and increased high frequency sound during crepuscular periods. The low frequency band was partitioned in two groups representative of soniferous reef fish, whereas the high frequency band represented snapping shrimp sound production. Daily daytime patterns in low-frequency sound production largely persisted in the weeks following the hurricane. Crepuscular sound production by snapping shrimp was maintained post-hurricane with only a small shift (~1.5dB) in the level of daytime vs nighttime sound production for this high frequency band. This study suggests that on short time scales, temporal patterns in the coral reef soundscape were relatively resilient to acoustic energy exposure during the storm, as well as changes in the benthic habitat and environmental conditions resulting from hurricane damage.

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“…These indices are some of the most widely used estimators in passive acoustic monitoring, and have been tested under diverse habitat and environmental conditions at global scale (e.g. Sueur et al 2014;Harris et al 2016;Moreno-G omez et al 2019). We calculated all eight acoustic indices for each 1-min recording, and used an upper limit of 12 kHz in most of them.…”

Section: Acoustic Indicesmentioning

confidence: 99%

“…Depraetere et al 2012;Gasc et al 2013;Duarte et al 2015). Justifications for excluding files with rain from analyses include: (1) sounds produced by rainfall either change behavior of singing animals or mask sounds of all biological diversity (Farina and Pieretti 2017;Rankin and Axel 2017;Moreno-G omez et al 2019), and (2) heavy rainfall results in higher amplitude background noises, concealing a considerable proportion of the frequency spectrum and thus generating biases in the values of several acoustic indices (Depraetere et al 2012;Gasc et al 2013;Duarte et al 2015).…”

Section: Introductionmentioning

confidence: 99%

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

Sánchez‐Giraldo

Bedoya

Morán-Vásquez

et al. 2020

Remote Sens Ecol Conserv

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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.

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Evaluating acoustic indices in the Valdivian rainforest, a biodiversity hotspot in South America

Cited by 33 publications

References 41 publications

Acoustic indices perform better when applied at ecologically meaningful time and frequency scales

Acoustic indices perform better when applied at ecologically meaningful time and frequency scales

Hurricane impacts on a coral reef soundscape

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

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