Automated detection of an insect‐induced keystone vegetation phenotype using airborne LiDAR

Wang, Zhengyang; Huben, Robert; Boucher, Peter B.; Van Amburg, Chase; Zeng, Jimmy; Chung, Nina; Wang, Jocelyn; King, Jeffrey; Knecht, Richard J.; Ng'iru, Ivy ORCID: https://orcid.org/0000-0002-4766-6251; Baraza, Augustine; Baker, Christopher C.M.; Martins, Dino J.; Pierce, Naomi E.; Davies, Andrew B.. 2024 Automated detection of an insect‐induced keystone vegetation phenotype using airborne LiDAR. Methods in Ecology and Evolution, 15 (5). 978-993. 10.1111/2041-210x.14298

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Abstract/Summary

1. Ecologists, foresters and conservation practitioners need ‘biodiversity scanners’ to effectively inventory biodiversity, audit conservation progress and track changes in ecosystem function. Quantifying biological diversity using remote sensing methods remains challenging, especially for small invertebrates. However, insect aggregations can drastically alter landscapes and vegetation, and these ‘extended phenotypes’ could serve as environmental landmarks of insect presence in remotely sensed data. 2. To test the feasibility of this approach, we studied symbiotic ants that alter the canopy shape of whistling thorn acacias (Acacia [syn. Vachellia] drepanolobium), a keystone tree species of the black cotton soils of east African savannas. We demonstrate a protocol for using light detection and ranging (LiDAR) data to collect, prepare (including a customizable tree-segmentation algorithm) and apply a convolutional neural network-based classification for the detection of ant-inhabited acacia tree phenotypic variations. Applying this protocol enabled us to effectively detect intra-specific tree phenotypic variation induced by insects. 3. Surveying ant occupancy across 16 ha and 9680 acacia trees took 1000 work hours, whereas surveyed patterns of ant distribution were replicated by our trained classifier using only an hour-long airborne LiDAR collection time. 4. We suggest that large-scale surveys of insect occupancy (including insect-vectored disease) can be automated through a combination of airborne LiDAR and machine learning.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1111/2041-210x.14298
UKCEH and CEH Sections/Science Areas:	Pollution (Science Area 2017-)
ISSN:	2041-210X
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	ant, extended phenotype, LiDAR, remote sensing, savanna
NORA Subject Terms:	Ecology and Environment Electronics, Engineering and Technology
Related URLs:	Dataset Other
Date made live:	13 Mar 2024 16:32 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537084

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