Efficacy of extracting indices from large‐scale acoustic recordings to monitor biodiversity |
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Authors: | Rachel T Buxton Mary Clapp Erik Meyer Erik Stabenau Lisa M Angeloni Kevin Crooks George Wittemyer |
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Affiliation: | 1. Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, ColoradoAuthors contributed equally to the study.;2. Evolution and Ecology Department, University of California, Davis, California;3. Sequoia & Kings Canyon National Parks, Three Rivers, California;4. Everglades National Park, South Florida Natural Resources Center, Homestead, Florida;5. Department of Biology, Colorado State University, Fort Collins, Colorado;6. Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado |
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Abstract: | Passive acoustic monitoring could be a powerful way to assess biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices (i.e., a mathematical summary of acoustic energy) offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examined the relationship between acoustic indices and the diversity and abundance of biological sounds in recordings. We reviewed the acoustic‐index literature and found that over 60 indices have been applied to a range of objectives with varying success. We used 36 of the most indicative indices to develop a predictive model of the diversity of animal sounds in recordings. Acoustic data were collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental United States. For terrestrial recordings, random‐forest models with a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R2 ≥ 0.94, mean squared error MSE] ≤170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively affected by insect, weather, and anthropogenic sounds. For marine recordings, random‐forest models poorly predicted Shannon diversity, richness, and total number of biological sounds (R2 ≤ 0.40, MSE ≥ 195). Our results suggest that using a combination of relevant acoustic indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats. |
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Keywords: | acoustic indices bioacoustics biodiversity passive acoustic monitoring random forest bioacú stica biodiversidad bosque aleatorio í ndices acú sticos monitoreo acú stico pasivo 声 学 指 标 生 物 多 样 性 生 物 声 学 无 源 声 音 监 测 随 机 森 林 |
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