Bioacoustics for species management: two case studies with a Hawaiian forest bird

Abstract

The management of animal endangered species requires detailed information on their distribution and abundance, which is often hard to obtain. When animals communicate using sounds, one option is to use automatic sound recorders to gather information on the species for long periods of time with low effort. One drawback of this method is that processing all the information manually requires large amounts of time and effort. Our objective was to create a relatively “user‐friendly” (i.e., that does not require big programming skills) automatic detection algorithm to improve our ability to get basic data from sound‐emitting animal species. We illustrate our algorithm by showing two possible applications with the Hawai'i ‘Amakihi, Hemignathus virens virens, a forest bird from the island of Hawai'i. We first characterized the ‘Amakihi song using recordings from areas where the species is present in high densities. We used this information to train a classification algorithm, the support vector machine (SVM), in order to identify ‘Amakihi songs from a series of potential songs. We then used our algorithm to detect the species in areas where its presence had not been previously confirmed. We also used the algorithm to compare the relative abundance of the species in different areas where management actions may be applied. The SVM had an accuracy of 86.5% in identifying ‘Amakihi. We confirmed the presence of the ‘Amakihi at the study area using the algorithm. We also found that the relative abundance of ‘Amakihi changes among study areas, and this information can be used to assess where management strategies for the species should be better implemented. Our automatic song detection algorithm is effective, “user‐friendly” and can be very useful for optimizing the management and conservation of those endangered animal species that communicate acoustically.

Measuring sound detection spaces for acoustic animal sampling and monitoring

“…Along the same line, variable detection spaces affect the results obtained from sampling birds in different habitat types (e.g. Wimmer et al, 2013;Sebastián-González et al, 2015), and they could even influence acoustic diversity indices in relation to vegetation structure (Pekin et al, 2012), since sites with higher sound transmission would have a higher chance of yielding a higher acoustic diversity.…”

Automated birdsong recognition in complex acoustic environments: a review

“…A few studies have attempted to apply these automatic detectors to real world data. For example, Sebastián-González et al (2015) used band-limited energy detector to detect call events of Hawai'i 'amakihi Hemignathus virens from field recordings with 93% recall but only 16.8% of them were good selections (precise endpoints). Duan et al (2013) configured the segmentation module separately for each of five species found in the dawn chorus (based on five hours of data).…”

Bioacoustic monitoring of animal vocal behavior for conservation

“…Since bioacoustic monitoring typically generates very large volumes of sound data, algorithms to detect vocalizations from sound files, termed call recognizers, are critical to the success of bioacoustics as a wildlife monitoring tool. Recognizer performance is also affected by the distance of the calling individual from the sound recorder, given loss of amplitude as well as attenuation of high frequency sound components (Digby, Towsey, Bell, & Teal, 2013;Heinicke et al, 2015;Sebastián-González et al, 2015). Performance depends in part on extraneous sources of sound (e.g., other species' calls) and the overall noisiness of the environment (e.g., anthropogenic noise, wind, rain), as well as the acoustic structure of the vocalizations, which is important in the choice of algorithm (Brandes, 2008;Cragg, Burger, & Piatt, 2015;Priyadarshani, Marsland, & Castro, 2018;Salamon et al, 2016;Towsey, Planitz, Nantes, Wimmer, & Roe, 2012).…”

“…In such cases, manual verification will most likely be necessary, the time commitments for which must be considered (Cragg et al, 2015;Rocha, Ferreira, Paula, Rodrigues, & Sousa-Lima, 2015). Moreover, packages like monitoR, as well as other commercial software, assist enormously in making recognizer development accessible to the non-expert (i.e., people without expert programming skills), which is crucial if bioacoustics is to become widely applied in threatened species monitoring (Priyadarshani et al, 2018;Sebastián-González et al, 2015). For this reason, recognizers should ideally be built to align with the project's aims (Priyadarshani et al, 2018), or should be easily altered as needs be.…”

“…Distinct from the related discipline ecoacoustics, bioacoustics is behavior-centric and focuses on the acoustic signals of individuals and species, rather than broader ecological processes or environments (Sueur & Farina, 2015;Towsey, Parsons, & Sueur, 2014). Potentially suited to any soundproducing species, especially those that are rare, cryptic or otherwise difficult to observe (Williams, O'Donnell, & Armstrong, 2018;Wrege, Rowland, Keen, & Shiu, 2017;Zwart, Baker, McGowan, & Whittingham, 2014), bioacoustic monitoring via autonomous recording units is becoming increasingly popular for measuring metrics such as species presence-absence (Sebastián-González, Pang-Ching, Barbosa, & Hart, 2015;Zwart et al, 2014), species richness (Celis-Murillo, Deppe, & Ward, 2012;Wimmer, Towsey, Roe, & Williamson, 2013), abundance (Borker et al, 2014;Jaramillo-Legorreta et al, 2017), and density (Efford, 2011;Efford & Fewster, 2013;Lambert & McDonald, 2014;Marques et al, 2013;Rogers, Ciaglia, Klinck, & Southwell, 2013;Stevenson et al, 2015).…”