Supplementary Material for "Automated detection of an insect-infested keystone vegetation phenotype using airborne LiDAR"

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 airborne LiDAR 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 LiDAR data. To test the feasibility of this approach, we studied the symbiotic ants that alter canopy shapes of whistling thorn acacia, a keystone tree species of the black cotton soils of east African savannas. We demonstrate a protocol of LiDAR data collection, training data preparation (including a customizable tree-segmentation algorithm) and convolutional neural network-based classification for the detection of ant-infested, within-species acacia tree phenotypic variations. Surveying ant occupancy of 402 hectares of 9,680 acacia trees took 1,000 work hours, while surveyed patterns of ant distribution was replicated by trained classifier based on an hour-long airborne LiDAR collection. We suggest that large scale surveys of insect occupancy (or insect-vectored disease) can be automated through a combination of airborne LiDAR and machine learning.生态学家、森林管理者与保护从业者亟需“生物多样性扫描仪”，以高效开展生物多样性清查、评估保护工作进展并监测生态系统功能变化。利用机载激光雷达（airborne LiDAR）量化生物多样性仍存在诸多挑战，针对小型无脊椎动物的量化更是如此。然而，昆虫集群会显著改变景观与植被结构，这类“扩展表型”可作为激光雷达数据中昆虫存在的环境标识物。为验证该方法的可行性，本研究聚焦于会改变哨刺金合欢树冠形态的共生蚂蚁——哨刺金合欢是东非稀树草原黑棉土中的关键树种。本研究提出一套完整流程：涵盖机载激光雷达数据采集、训练数据制备（含可自定义的树木分割算法）以及基于卷积神经网络（convolutional neural network）的分类模型，用于检测受蚂蚁侵染的种内金合欢表型变异。本次研究共对402公顷范围内的9680棵金合欢树开展蚂蚁栖息状况调查，耗时1000个工时；而基于1小时机载激光雷达扫描数据训练得到的分类器，即可复现调查获取的蚂蚁分布模式。本研究表明，结合机载激光雷达与机器学习技术，可实现大规模昆虫栖息状况（或虫媒疾病）调查的自动化。