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

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.