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PMID: 35636745 Published · ppublish English

Revealing the real-time diversity and abundance of small mammals by using an Intelligent Animal Monitoring System (IAMS).

Integrative zoology ·Vol. 17 ·No. 6 ·2022-11-00

Yang X, Han L, Wang Y, Guo C, Zhang Z

Abstract

It is challenging to reveal the real-time spatio-temporal change of diversity and abundance of animals in natural systems by using traditional methods. The rapid advancement of new technologies such as the Internet of Things, artificial intelligence, and big-data processing, provide opportunities for developing novel technologies for monitoring biodiversity and population abundance of animals with high efficacy and accuracy. In this study, by using a recently developed Intelligent Animal Monitoring System, named "Vector Intelligent Monitoring System (VIMS)", we investigated the real-time diversity and abundance of small mammals in the Banruosi forest, Dujiangyan region, southwest China. To make a comparison of the VIMS with traditional methods, we also surveyed the diversity and abundance of small mammals using wired live traps. Compared to live traps, the VIMS has several advantages such as automatic data collection, intelligent identification of species, data visualization, whole-day and all-weather operation, little disturbance to animals, real-time monitoring, and is capable of revealing more small mammal species. However, the VIMS also has several disadvantages over live traps such as lower trapping efficiency and being more expensive than live traps. Our results suggest that the VIMS can be a complementary method to traditional ones in monitoring the real-time spatio-temporal change of diversity and abundance of small mammals (especially rare species). In addition, the VIMS is useful in monitoring other small animals like small carnivores, birds, amphibians, and reptiles.

Keywords
Internet of Things (IoT) artificial intelligence (AI) live trap small mammal species diversity
MeSH 主题词
Animals Artificial Intelligence Mammals Biodiversity Forests China
Article Info
Journal
Integrative zoology
Abbr.
Integr Zool
ISSN
1749-4877
Published
2022-11-00
Language
English
Country/Region
Australia
NLM ID
101492420
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