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

Author:

Wang Zhengyang123ORCID,Huben Robert3,Boucher Peter B.1ORCID,Van Amburg Chase1ORCID,Zeng Jimmy3ORCID,Chung Nina1ORCID,Wang Jocelyn1ORCID,King Jeffrey3,Knecht Richard J.14ORCID,Ng'iru Ivy567ORCID,Baraza Augustine5,Baker Christopher C. M.1ORCID,Martins Dino J.58ORCID,Pierce Naomi E.14ORCID,Davies Andrew B.1ORCID

Affiliation:

1. Department of Organismic and Evolutionary Biology Harvard University Cambridge Massachusetts USA

2. College of Life Sciences Sichuan University Chengdu China

3. Insect Renaissance Group Boston Massachusetts USA

4. Museum of Comparative Zoology Harvard University Cambridge Massachusetts USA

5. Mpala Research Centre Nanyuki Laikipia Kenya

6. School of Biosciences Cardiff University Cardiff UK

7. UK Centre for Ecology and Hydrology Wallingford UK

8. Turkana Basin Institute Stony Brook University Stony Brook New York USA

Abstract

Abstract 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. 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. 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. 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.

Publisher

Wiley

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