Abstract
Passive acoustic monitoring is a potentially valuable tool in biodiversity hotspots, where surveying can occur at large scales across land conversion types. However, in order to extract meaningful biological information from resulting enormous acoustic datasets, rapid analytical techniques are required. Here we tested the ability of a suite of acoustic indices to predict avian bioacoustic activity in recordings collected from the Western Ghats, a biodiversity hotspot in southwestern India. Recordings were collected at 28 sites in a range of land-use types, from tea, coffee, and cardamom plantations to remnant forest stands. Using 36 acoustic indices we developed random forest models to predict the richness, diversity, and total number of avian vocalizations observed in recordings. We found limited evidence that acoustic indices predict the richness and total number of avian species vocalizations in recordings (R2 < 0.51). However, acoustic indices predicted the diversity of avian species vocalizations with high accuracy (R2 = 0.64, mean squared error = 0.17). Index models predicted low and high diversity best, with the highest residuals for medium diversity values and when continuous biological sounds were present (e.g., insect sounds >8 sec). The acoustic complexity index and roughness index were the most important for predicting avian vocal diversity. Avian species richness was generally higher among shade-grown crops than in the open tea plantation. Our results suggest that models incorporating acoustic indices can accurately predict low and high avian species diversity from acoustic recordings. Thus, ecoacoustics could be an important contributor to biodiversity monitoring across landscapes like the Western Ghats, which are a complex mosaic of different land-use types and face continued changes in the future.
Highlights
Rapid methods to assess biodiversity and ecosystem health at large spatial and temporal scales are paramount for informing conservation planning (Stem et al, 2005)
We found that indices were of limited utility to capture the richness of avian species vocalizations, but accurately reflected the Shannon diversity of avian vocalizations in recordings (Table 2)
We found that acoustic descriptors, or acoustic indices summarizing sound energy, were less important in the model predicting diversity of avian vocalizations, likely because they are less effective at capturing acoustic heterogeneity
Summary
Rapid methods to assess biodiversity and ecosystem health at large spatial and temporal scales are paramount for informing conservation planning (Stem et al, 2005). Because sound-producing species are important indicators of environmental health, acoustic surveys offer an important approach for biodiversity monitoring programs (Gregory and Strien, 2010; Blumstein et al, 2011). Passive acoustic monitoring would be useful in biodiversity hotspots, where understanding the impact of widespread land conversion on numerous species requires rapid surveying at a large scale. In order to extract meaningful biological information from the enormous datasets that result from large-scale acoustic monitoring, a number of acoustic indices have been developed (Buxton et al, in review). Acoustic indices examine the heterogeneity of the acoustic environment, under the assumption that more species found in a community will produce a greater number of different signals at the same time (Sueur et al, 2008b). The development and standardization of appropriate acoustic indices have occurred predominantly in temperate regions (Buxton et al, in review)
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