Xishuangbanna Botanical Garden makes progress in the research of bat automatic monitoring technology

[ Instrument Network Instrument Development ] With the development of human society and economy, human activities and climate change have had a profound impact on biodiversity. Bat is one of the most important ecological indicator animals, and plays an important role in the ecosystem. It is the key research object of ecological protection. The most typical feature of bats is the ability to emit ultrasound for environmental detection and predation. They are widely distributed around the world, with more than 1,350 species and extremely rich biodiversity.
At present, the survey method of bat diversity is artificially set up fog net and harp net. There are many uncertainties in this method, mainly because Xishuangbanna area is located in the tropics, the species is widely distributed and uneven, and the investigation of the area is seriously lacking and species. Due to the high complexity of sound waves, the uncertainty of this survey method has largely limited the discussion of issues such as ecosystem protection.
In order to simultaneously improve the efficiency of bat diversity surveys on the spatial and temporal scales, the Landscape Ecology Research Group of the Xishuangbanna Tropical Botanical Garden Comprehensive Protection Center of the Chinese Academy of Sciences and the School of Software of Yunnan University have developed a software to automatically process a large number of ultrasonic monitoring data. The software uses the artificial intelligence algorithm (Artifitall intelligencet) to extract signal features from nearly a thousand bat ultrasonic audio data in Xishuangbanna, Thailand and Malaysia.
Secondly, using the self-developed deep learning network model to optimize the parameters, simulate the optimal model parameters, and improve the accuracy of automatic recognition to over 90%. This model is the first artificial intelligence algorithm based tropical bat ultrasonic analysis software. The researchers adjusted the software to the field, greatly reducing the error rate of field data identification, further expanding the applicable area of ​​the model, and helping to achieve continuous monitoring in four seasons a year.
The research results not only greatly improved the efficiency of the bat diversity survey, but also provided a standard data processing solution for future wildlife acoustic surveys, including birds and amphibians. This data collection scheme and standard data analysis methods can improve the setting of future protection goals. The research results were published online in the ecological conservation journal Biological Conservation under the topic of Automatic standardized processing and identification of tropical bat calls using deep learning approaches.
Xi Xingbanna Botanical Garden Chen Xing and Yunnan University Software College Zhao are the co-first authors of the article, Xishuangbanna Botanical Garden Chen Yanhua as co-author, Xishuangbanna Botanical Garden Associate Researcher Alice C. Hughes and Yunnan University Software College Professor Zhou Wei as co-author.
Modeling and testing flow chart for bat ultrasonic recognition software Waveman. Modeling is roughly divided into three steps: audio database construction, image transformation, and training model. Tested with two data types, including image data and data from unprocessed audio in the field.

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