Skip to main content

Advertisement

SpringerLink
Log in

Deep learning improves acoustic biodiversity monitoring and new candidate forest frog species identification (genus Platymantis) in the Philippines

  • Original Paper
  • Published:
Biodiversity and Conservation Aims and scope Submit manuscript

Abstract

One significant challenge to biodiversity assessment and conservation is persistent gaps in species diversity knowledge in Earth’s most biodiverse areas. Monitoring devices that utilize species-specific advertisement calls show promise in overcoming challenges associated with lagging frog species discovery rates. However, these devices generate data at paces faster than it can be analyzed. As such, automated platforms capable of efficient data processing and accurate species-level identification are at a premium. In addressing this gap, we used TensorFlow Inception v3 to design a robust, automated species identification system for 41 Philippine frog species (genus Platymantis), utilizing single-note audio spectrograms. With this model, we explored two concepts: (1) performance of our deep-learning model in discriminating closely-related frog species based on images representing advertisement call notes, and (2) the potential of this platform to accelerate new species discovery. TensorFlow identified species with a ~ 94% overall correct identification rate. Incorporating distributional data increased the overall identification rate to ~ 99%. In applying TensorFlow to a dataset that included undescribed species in addition to known species, our model was able to differentiate undescribed species through variation in “certainty” rate; the overall certainty rate for undescribed species was 65.5% versus 83.6% for described species. This indicates that, in addition to discriminating recognized frog species, our model has the potential to flag possible new species. As such, this work represents a proof-of-concept for automated, accelerated detection of novel species using acoustic mate-recognition signals, that can be applied to other groups characterized by vibrational cues, seismic signals, and vibrational mate-recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

Data are publicly available via Cornell University’s Laboratory of Ornithology and Macaulay Library of Natural Sounds (https://www.macaulaylibrary.org).

References

  • Abadi M et al. (2016) Tensorflow: a system for large-scale machine learning. Paper presented at the 12th USENIX Symposium on Operating Systems Design and Implementation, Savannah, USA,

  • Aide TM, Corrada-Bravo C, Campos-Cerqueira M, Milan C, Vega G, Alvarez R (2013) Real-time bioacoustics monitoring and automated species identification. PeerJ 1:e103

    Article  PubMed  PubMed Central  Google Scholar 

  • Araya-Salas M, Smith-Vidaurre G (2017) warbleR: an R package to streamline analysis of animal acoustic signals. Methods Ecol Evol 8:184–191

    Article  Google Scholar 

  • Brabant R, Laurent Y, Dolap U, Degraer S, Poerink BJ (2018) Comparing the results of four widely used automated bat identification software programs to identify nine bat species in coastal Western Europe Belgian. J Zool 148:119–128

    Google Scholar 

  • Brown RM, Gonzalez JC (2007) A new forest frog of the genus Platymantis (Amphibia: Anura: Ranidae) from the Bicol Peninsula of Luzon Island, Philippines. Copeia 2007:251–266

    Article  Google Scholar 

  • Brown R, Stuart B (2012) Patterns of biodiversity discovery through time: an historical analysis of amphibian species discoveries in the Southeast Asian mainland and island archipelagos. In: Gower D et al (eds) Biotic evolution and environmental change in Southeast Asia. Cambridge University Press, Cambridge, pp 348–389

    Chapter  Google Scholar 

  • Brown WC, Alcala A, Diesmos AC (1997a) A new species of the genus Platymantis (Amphibia: Ranidae) from Luzon Island, Philippines. Proc Biol Soc Wash 110:18–23

    Google Scholar 

  • Brown WC, Alcala A, Diesmos AC, Alcala E (1997b) Species of the guentheri group of Platymantis (Amphibia: Ranidae) from the Philippines, with descriptions of four new species. Proc Calif Acad Sci 50:1–20

    Google Scholar 

  • Brown W, Alcala A, Ong P, Diesmos A (1999a) A new species of Platymantis (Amphibia: Ranidae) from the Sierra Madre Mountains, Luzon Island, Philippines. Proc Biol Soc Wash 112:510–514

    Google Scholar 

  • Brown WC, Alcala AC, Diesmos AC (1999b) Four new species of the genus Platymantis (Amphibia: Ranidae) from Luzon Island, Philippines. Proc Calif Acad Sci 51:449–460

    Google Scholar 

  • Brown RM et al (2013) Evolutionary processes of diversification in a model island archipelago. Annu Rev Ecol Evol Syst 44:411–435

    Article  Google Scholar 

  • Brown RM, De Layola LA, Lorenzo A, Diesmos MLL, Diesmos AC (2015a) A new species of limestone karst inhabiting forest frog, genus Platymantis (Amphibia: Anura: Ceratobatrachidae: subgenus Lupacolus) from southern Luzon Island, Philippines. Zootaxa 4048:191–210

    Article  PubMed  Google Scholar 

  • Brown RM, Siler CD, Richards SJ, Diesmos AC, Cannatella DC (2015b) Multilocus phylogeny and a new classification for Southeast Asian and Melanesian forest frogs (family Ceratobatrachidae). Zool J Linnean Soc 174:130–168

    Article  Google Scholar 

  • Bryan JE, Shearman PL, Asner GP, Knapp DE, Aoro G, Lokes B (2013) Extreme differences in forest degradation in Borneo: coamparing practices in Sarawak, Sabah, and Brunei. PLoS ONE 8:e69679

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chan KO, Brown RM (2017) Did true frogs ‘dispersify’? Biol Lett 13:20170299

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen Z, Wiens JJ (2020) The origins of acoustic communication in vertebrates. Nat Commun 11:1–8

    Google Scholar 

  • Diesmos AC et al (2015) Amphibians of the Philippines, part I: checklist of the species. Proc Calif Acad Sci 62:457–539

    Google Scholar 

  • Feinberg JA et al (2014) Cryptic diversity in metropolis: confirmation of a new leopard frog species (Anura: Ranidae) from New York City and surrounding Atlantic coast regions. PLoS ONE 9:e108213

    Article  PubMed  PubMed Central  Google Scholar 

  • Gaston KJ, O’Neill MA (2004) Automated species identification: why not? Philos Trans R Soc Lond B 359:655–667

    Article  Google Scholar 

  • Gerhardt HC (1978) Mating call recognition in the green treefrog (Hyla cinerea): the significance of some fine-temporal properties. J Exp Biol 74:59–73

    Article  Google Scholar 

  • Gerhardt HC (1994) The evolution of vocalization in frogs and toads. Annu Rev Ecol Syst 25:293–324

    Article  Google Scholar 

  • González-del-Pliego P, Freckleton RP, Edwards DP, Koo MS, Scheffers BR, Pyron RA, Jetz W (2019) Phylogenetic and trait-based prediction of extinction risk for data-deficient amphibians. Curr Biol 29(1557–1563):e1553

    Google Scholar 

  • Guirado E, Tabik S, Rivas ML, Alcaraz-Segura D, Herrera F (2019) Whale counting in satellite and aerial images with deep learning. Sci Rep 9:14259

    Article  PubMed  PubMed Central  Google Scholar 

  • Gurgel-Gonçalves R et al (2017) Automated identification of insect vectors of chagas disease in Brazil and Mexico: the virtual vector. Lab PeerJ 5:e3040

    Article  PubMed  Google Scholar 

  • Holmgren J, Persson Å, Söderman U (2008) Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images. Int J Remote Sens 29:1537–1552

    Article  Google Scholar 

  • Inger RF (1954) Systematics and zoogeography of Philippine Amphibia. Fieldiana Zool 33:181–531

    Google Scholar 

  • Khalighifar A, Komp E, Ramsey JM, Gurgel-Gonçalves R, Peterson AT (2019) Deep learning algorithms improve automated identification of chagas disease vectors. J Med Entomol 56:1404–1410

    Article  PubMed  Google Scholar 

  • Klein DJ, McKown MW, Tershy BR (2015) Deep learning for large scale biodiversity monitoring. Paper presented at the Bloomberg Data for Good Exchange Conference, New York City, USA

  • Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International Joint Conferences on Artificial Intelligence, Montreal, Canada, pp. 1137–1145

  • Kumar N, Belhumeur PN, Biswas A, Jacobs DW, Kress WJ, Lopez IC, Soares JV (2012) Leafsnap: A computer vision system for automatic plant species identification. In: European Conference on Computer Vision, Florence, Italy, pp. 502–516

  • MacLeod N, Benfield M, Culverhouse P (2010) Time to automate identification. Nature 467:154–155

    Article  CAS  PubMed  Google Scholar 

  • Molinaro AM, Simon R, Pfeiffer RM (2005) Prediction error estimation: a comparison of resampling methods. Bioinformatics 21:3301–3307

    Article  CAS  PubMed  Google Scholar 

  • Narins PM, Capranica RR (1977) An automated technique for analysis of temporal features in animal vocalizations. Anim Behav 25:615–621

    Article  Google Scholar 

  • Philippe J, Felipe L, Celio F (2017) The use of bioacoustics in anuran taxonomy: theory, terminology, methods and recommendations for best practice. Zootaxa 4251:1–124

    Article  Google Scholar 

  • Ramcharan A, Baranowski K, McCloskey P, Ahmed B, Legg J, Hughes DP (2017) Deep learning for image-based cassava disease detection. Front Plant Sci 8:1852–1859

    Article  PubMed  PubMed Central  Google Scholar 

  • Russakovsky O et al (2015) ImageNet large scale visual recognition challenge. Int J Comput Vision 115:211–252

    Article  Google Scholar 

  • Rzanny M, Seeland M, Wäldchen J, Mäder P (2017) Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain. Plant Methods 13:1–11

    Article  Google Scholar 

  • Scheffers BR, Brunner RM, Ramirez SD, Shoo LP, Diesmos A, Williams SE (2013) Thermal buffering of microhabitats is a critical factor mediating warming vulnerability of frogs in the Philippine biodiversity hotspot. Biotropica 45:628–635

    Article  Google Scholar 

  • Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  PubMed  Google Scholar 

  • Siler CD, Linkem CW, Diesmos AC, Alcala AC (2007) A new species of Platymantis (Amphibia: Anura: Ranidae) from Panay Island, Philippines. Herpetologica 63:351–364

    Article  Google Scholar 

  • Siler CD, Diesmos AC, Linkem CW, Diesmos ML, Brown RM (2010) A new species of limestone-forest frog, genus Platymantis (Amphibia: Anura: Ceratobatrachidae) from central Luzon Island, Philippines. Zootaxa 2482:49–63

    Article  Google Scholar 

  • Smith SW et al (2019) A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation. J Electrocardiol 52:88–95

    Article  PubMed  Google Scholar 

  • Sueur J, Aubin T, Simonis C (2008) Seewave, a free modular tool for sound analysis and synthesis. Bioacoustics 18:213–226

    Article  Google Scholar 

  • Sugai LSM, Silva TSF, Ribeiro JW Jr, Llusia D (2019) Terrestrial passive acoustic monitoring: review and perspectives. Bioscience 69:15–25

    Article  Google Scholar 

  • Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA,

  • Tapley B, Michaels CJ, Gumbs R, Böhm M, Luedtke J, Pearce-Kelly P, Rowley JJ (2018) The disparity between species description and conservation assessment: a case study in taxa with high rates of species discovery. Biol Conserv 220:209–214

    Article  Google Scholar 

  • Taylor EH (1923) Addition to the herpetological fauna of the Philippine Islands III Philippine. J Sci 22:515–557

    Google Scholar 

  • Tonini JFR, Provete DB, Maciel NM, Morais AR, Goutte S, Toledo LF, Pyron RA (2020) Allometric escape from acoustic constraints is rare for frog calls. Ecol Evol 10:1–10

    Article  Google Scholar 

  • Vieites DR, Wollenberg KC, Andreone F, Köhler J, Glaw F, Vences M (2009) Vast underestimation of Madagascar’s biodiversity evidenced by an integrative amphibian inventory. Proc Natl Acad Sci USA 106:8267–8272

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Vignal C, Kelley D (2007) Significance of temporal and spectral acoustic cues for sexual recognition in Xenopus laevis. Proc R Soc B 274:479–488

    Article  PubMed  Google Scholar 

  • Villa AG, Salazar A, Vargas F (2017) Towards automatic wild animal monitoring: identification of animal species in camera-trap images using very deep convolutional neural networks. Ecol Inform 41:24–32

    Article  Google Scholar 

  • Wells KD (2010) The ecology and behavior of amphibians. University of Chicago Press, Chicago

    Google Scholar 

  • Wells KD, Schwartz JJ (2007) The behavioral ecology of anuran communication. Hearing and sound communication in amphibians, vol 28. Springer, New York, pp 44–86

    Chapter  Google Scholar 

  • Wimmer J, Towsey M, Roe P, Williamson I (2013) Sampling environmental acoustic recordings to determine bird species richness. Ecol Appl 23:1419–1428

    Article  PubMed  Google Scholar 

  • Zhao Z, Zhang S, Xu Z, Bellisario K, Dai N, Omrani H, Pijanowski BC (2017) Automated bird acoustic event detection and robust species classification. Ecol Inform 39:99–108

    Article  Google Scholar 

Download references

Acknowledgements

Philippine Platymantis frog calls were collected with support from the U. S. National Science Foundation’s former Doctoral Dissertation Improvement Grant (DEB 0073199; 2001–2003) and a Biotic Surveys and Inventories Grant (DEB 0743491; 2008–2012). Collection and co-curated voucher specimens and their associated digital media specimens, archival digitization, data verification, and online serving of digital media specimens was made possible by a NSF Thematic Collections Network (TCN) program Grant (DEB 1304585; 2013–2018). Recent extended specimen collection and curation has been supported by NSF DEB 1654388 and 1557053, with further support from the KU Biodiversity Institute’s Rudkin Research Exploration (REX) Fund and KU College of Liberal Arts and Sciences Docking Scholar Fund. We thank A. Diesmos, J. Fernandez, C. Siler, and C. Meneses for help recording frogs.

Funding

No funding was used for this research.

Author information

Author notes

  1. Ali Khalighifar

    Present address: Warner College of Natural Resources, Colorado State University, Fort Collins, CO, USA

Authors and Affiliations

  1. Biodiversity Institute, University of Kansas, Lawrence, KS, USA

    Ali Khalighifar, Rafe M. Brown, Johana Goyes Vallejos & A. Townsend Peterson

  2. Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, USA

    Ali Khalighifar, Rafe M. Brown & A. Townsend Peterson

  3. Division of Biological Sciences, University of Missouri, Columbia, MO, USA

    Johana Goyes Vallejos

Authors
  1. Ali Khalighifar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafe M. Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Johana Goyes Vallejos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Townsend Peterson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Khalighifar.

Ethics declarations

Conflict of interest

All authors declare that they have no conflicts of interest.

Additional information

Communicated by Dirk Sven Schmeller.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Below is the link to the Supplementary information.

Supplementary information 1 (DOCX 17 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khalighifar, A., Brown, R.M., Goyes Vallejos, J. et al. Deep learning improves acoustic biodiversity monitoring and new candidate forest frog species identification (genus Platymantis) in the Philippines. Biodivers Conserv 30, 643–657 (2021). https://doi.org/10.1007/s10531-020-02107-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10531-020-02107-1

Keywords

Search

Navigation