# A Collection of Best Practices for the Collection and Analysis of Bioacoustic Data
## 4 Ecosystem Biodiversity
Acoustic richness (AR), representing traditional alpha diversity (which measures the diversity within an area), was calculated as a rank statistic, by estimating the samples' spectral and temporal entropy. Acoustic dissimilarity, representing traditional beta diversity (which measures differences between areas), was calculated by estimating the spectral and temporal dissimilarity among sites in samples recorded at the same time on the same day. [PERSON] et al. [69] then compared these acoustic metric outcomes to those generated from more traditional biodiversity metrics estimated using species inventories based on aural identification. They found that the results of the AR analysis matched those obtained using aural identification of species in this habitat and that the two acoustic indices (AR and acoustic dissimilarity) provided complementary information. Based on these results, the authors concluded that acoustic indices can be used to rapidly assess relative spatial and diel variability in biodiversity in temperate woodlands. Before drawing inferences based on their results, the authors of this study were careful to address sources of bias where possible, and to constrain their research questions to those that could be answered using the type of data collected. They controlled for instrument bias by using the same recorders at each site. Environmental bias was normalized across sites by placing the recorders in the same height and position relative to each other and by not using recordings from days when bad weather (wind, rain) degraded sound recording quality, which would have limited the ability to conduct accurate acoustic estimates of diversity. Non-signal-related noise was removed using both a bandpass and an SNR filter. Temporal variability was accounted for by collecting a large sample size for both morning and evening time frames. These authors also offer recommendations for improving the accuracy of acoustic biodiversity metrics. Notably, acoustic samples will capture not just the focal species but also other types of sounds, both biotic and abiotic, which need to be accounted for before processing. Additionally, a trained observer may be able to recognize multiple call types belonging to a given species, and thus avoid artificially inflating the acoustic measure of species diversity. As an illustration of this issue, the authors mention that some amphibians were recorded during the evening at one site, thus spuriously inflating the inferred count of bird species diversity at that site. This observation highlights the importance of understanding the composition of an ecosystem before interpreting acoustic biodiversity analyses, to avoid unfounded inferences. It is also important to note that even trained observers cannot always identify all call types to species. In these cases, it can be important to employ a statistical or machine learning classifier as described in Section 1, Species Identification. In either case, one must quantify the reliability and accuracy of methods used to identify species, and uncertainty in species identification should be built into methods used to estimate biodiversity. Finally, traditional analyses of species richness often account for varying species-specific probabilities of detection [112; 113]. Acoustic detection probabilities are currently unknown for many species, and this will be a productive topic for further exploration. In particular, it will be useful to characterize the impacts of differential likelihoods of encountering different species, how often or systematically they produce acoustic signals, the structure of their different types of vocalizations, and dynamic local environment effects on sound attenuation or propagation. All of these parameters affect probabilities that any given species will be detected, and thus impact the acoustic assessment of biodiversity.