Acoustic traits of bat-pollinated flowers compared to flowers of other pollination syndromes and their echo-based classification using convolutional neural networks

Bat-pollinated flowers have to attract their pollinators in absence of light and therefore some species developed specialized echoic floral parts. These parts are usually concave shaped and act like acoustic retroreflectors making the flowers acoustically conspicuous to the bats. Acoustic plant spec...

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Bibliographic Details
Published in:	PLoS computational biology Vol. 17; no. 12; p. e1009706
Main Authors:	Simon, Ralph, Bakunowski, Karol, Reyes-Vasques, Angel Eduardo, Tschapka, Marco, Knörnschild, Mirjam, Steckel, Jan, Stowell, Dan
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 16-12-2021 Public Library of Science (PLoS)
Subjects:	Acoustic properties Acoustics Animals Bats Biology and Life Sciences Botanical research Chiroptera - physiology Echolocation - physiology Environmental aspects Flowers Flowers - classification Flowers - physiology Genetic aspects Neural networks Neural Networks, Computer Physical Sciences Physiological aspects Plant-animal interactions Pollination Pollination - physiology Sound Spectrography Belgium
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Summary:	Bat-pollinated flowers have to attract their pollinators in absence of light and therefore some species developed specialized echoic floral parts. These parts are usually concave shaped and act like acoustic retroreflectors making the flowers acoustically conspicuous to the bats. Acoustic plant specializations only have been described for two bat-pollinated species in the Neotropics and one other bat-dependent plant in South East Asia. However, it remains unclear whether other bat-pollinated plant species also show acoustic adaptations. Moreover, acoustic traits have never been compared between bat-pollinated flowers and flowers belonging to other pollination syndromes. To investigate acoustic traits of bat-pollinated flowers we recorded a dataset of 32320 flower echoes, collected from 168 individual flowers belonging to 12 different species. 6 of these species were pollinated by bats and 6 species were pollinated by insects or hummingbirds. We analyzed the spectral target strength of the flowers and trained a convolutional neural network (CNN) on the spectrograms of the flower echoes. We found that bat-pollinated flowers have a significantly higher echo target strength, independent of their size, and differ in their morphology, specifically in the lower variance of their morphological features. We found that a good classification accuracy by our CNN (up to 84%) can be achieved with only one echo/spectrogram to classify the 12 different plant species, both bat-pollinated and otherwise, with bat-pollinated flowers being easier to classify. The higher classification performance of bat-pollinated flowers can be explained by the lower variance of their morphology.
Bibliography:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 The authors have declared that no competing interests exist. JS and DS also contributed equally to this work.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1009706