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The Necessity, Promise and Challenge of Automated Biodiversity Surveys

  • Justin Kitzes (a1) and Lauren Schricker (a1)

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Corresponding author

Author for correspondence: Justin Kitzes, Email: justin.kitzes@pitt.edu

References

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Buxton, RT, Lendrum, PE, Crooks, KR, Wittemyer, G (2018) Pairing camera traps and acoustic recorders to monitor the ecological impact of human disturbance. Global Ecology and Conservation 16: e00493.
Corrada Bravo, CJ, Álvarez Berríos, R, Aide, TM (2017) Species-specific audio detection: a comparison of three template-based detection algorithms using random forests. PeerJ Computer Science 3: e113.
GBIF (2019) Global Biodiversity Information Facility. Free and Open Access to Biodiversity Data [www document]. URL https://www.gbif.org/
Hill, AP, Prince, P, Pinña Covarrubias, E, Doncaster, CP, Snaddon, JL, Rogers, A (2018) AudioMoth: evaluation of a smart open acoustic device for monitoring biodiversity and the environment. Methods in Ecology and Evolution 9: 11991211.
iNaturalist (2019) iNaturalist Computer Vision Explorations [www document]. URL https://www.inaturalist.org/pages/computer_vision_demo
LifeCLEF (2019) BirdCLEF 2018 | ImageCLEF/LifeCLEF – Multimedia Retrieval in CLEF [www document]. URL https://www.imageclef.org/node/230
Marconi, S, Graves, SJ, Gong, D, Nia, MS, Le Bras, M, Dorr, BJ, et al. (2019) A data science challenge for converting airborne remote sensing data into ecological information. PeerJ 6: e5843.
Microsoft (2019) AI for Earth – APIs and Applications: Species Classifications [www document]. URL https://www.microsoft.com/en-us/ai/ai-for-earth-apis?activetab=pivot1:primaryr4
Norouzzadeh, MS, Nguyen, A, Kosmala, M, Swanson, A, Palmer, MS, Packer, C, Clune, J (2018) Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences of the United States of America 115(25): E5716.
Priyadarshani, N, Marsland, S, Castro, I (2018) Automated birdsong recognition in complex acoustic environments: a review. Journal of Avian Biology 49: e01447.
Steenweg, R, Hebblewhite, M, Kays, R, Ahumada, J, Fisher, JT, Burton, C, et al. (2017) Scaling up camera traps: monitoring the planet’s biodiversity with networks of remote sensors. Frontiers in Ecology and the Environment 15(1): 2634.
Stowell, D, Wood, MD, Pamuła, H, Stylianou, Y, Glotin, H (2019) Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge. Methods in Ecology and Evolution 10(3): 368380.
Sugai, LSM, Silva, TSF, Ribeiro, JJW, Llusia, D (2018) Terrestrial passive acoustic monitoring: review and perspectives. Bioscience 69(1): 1525.
Towsey, M, Parsons, S, Sueur, J (2014) Ecology and acoustics at a large scale. Ecological Informatics 21: 13.
USFWS (2019) USFWS: Indiana Bat Summer Survey Guidance – Automated Acoustic Bat ID Software Programs [www document]. URL https://www.fws.gov/midwest/endangered/mammals/inba/surveys/inbaacousticsoftware.html
USGS (2017) North American Breeding Bird Survey Summary of Effort in 2017 [www document]. URL https://www.pwrc.usgs.gov/BBS/Results/Summaries/
Zuur, AF, Ieno, EN, Walker, NJ, Saveliev, AA, Smith, GM (2009) Mixed Effects Models and Extensions in Ecology in R . New York, NY, USA: Springer Science+Business Media.

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