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Training data for ˊōmaˊo (Myadestes obscurus) songs in Hakalau Forest NWR, Hawaiˊi, data collected in 2015

Dates

Publication Date
Start Date
2015-07-27
End Date
2015-08-17

Citation

Camp, R.J., and Sebastian-Gonzalez, Esther, 2018, Hakalau Bioacoustic Surveys and Models 2015 (ver. 1.1, August 2018): U.S. Geological Survey data release, https://doi.org/10.5066/F7PZ571Q.

Summary

Training data used in our automatic detection algorithm; see Sebastian-Gonzalez et al. (2015) for details. Sebastián-González, Esther, Pang-Ching, Joshua, Barbosa, J.M., and Hart, P.J., 2015, Bioacoustics for species management: Two case studies with a Hawaiian forest bird, Ecol Evol. 5:4696–4705, https://dx.doi.org/10.1002/ece3.1743.

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Training.omao.song.csv 552.19 KB
Training.omao.song.xml
Original FGDC Metadata

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Purpose

We describe an automatic detection algorithm to improve our ability to get basic data from sound-emitting animal species. We show how our algorithm can be used to detect presence and compare relative abundance for the Hawai‘i ‘Amakihi, a forest bird from the island of Hawai‘i. 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.

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  • Pacific Island Ecosystems Research Center

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