Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain, State Key Laboratory of Genetic Resources and Evolution, Chinese Academy of Sciences, Kunming, Yunnan 650223, People's Republic of China.
Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, People's Republic of China.
Philos Trans R Soc Lond B Biol Sci. 2024 Jun 24;379(1904):20230123. doi: 10.1098/rstb.2023.0123. Epub 2024 May 6.
Arthropods contribute importantly to ecosystem functioning but remain understudied. This undermines the validity of conservation decisions. Modern methods are now making arthropods easier to study, since arthropods can be mass-trapped, mass-identified, and semi-mass-quantified into 'many-row (observation), many-column (species)' datasets, with homogeneous error, high resolution, and copious environmental-covariate information. These 'novel community datasets' let us efficiently generate information on arthropod species distributions, conservation values, uncertainty, and the magnitude and direction of human impacts. We use a DNA-based method (barcode mapping) to produce an arthropod-community dataset from 121 Malaise-trap samples, and combine it with 29 remote-imagery layers using a deep neural net in a joint species distribution model. With this approach, we generate distribution maps for 76 arthropod species across a 225 km temperate-zone forested landscape. We combine the maps to visualize the fine-scale spatial distributions of species richness, community composition, and site irreplaceability. Old-growth forests show distinct community composition and higher species richness, and stream courses have the highest site-irreplaceability values. With this 'sideways biodiversity modelling' method, we demonstrate the feasibility of biodiversity mapping at sufficient spatial resolution to inform local management choices, while also being efficient enough to scale up to thousands of square kilometres. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.
节肢动物对生态系统功能有重要贡献，但研究不足。这削弱了保护决策的有效性。现代方法现在使研究节肢动物变得更容易，因为节肢动物可以被大规模捕获、大规模识别，并半大规模量化为“多行（观察）、多列（物种）”数据集，具有均匀的误差、高分辨率和丰富的环境协变量信息。这些“新型群落数据集”使我们能够有效地生成有关节肢动物物种分布、保护价值、不确定性以及人类影响的大小和方向的信息。我们使用基于 DNA 的方法（条形码映射）从 121 个玛氏陷阱样本中生成一个节肢动物群落数据集，并使用深度神经网络将其与 29 个远程图像层结合在一个联合物种分布模型中。通过这种方法，我们生成了 76 种节肢动物在 225 公里温带森林景观中的分布地图。我们将这些地图结合起来，可视化物种丰富度、群落组成和地点不可替代性的细粒度空间分布。原始森林显示出明显的群落组成和更高的物种丰富度，而溪流地段具有最高的地点不可替代性值。通过这种“横向生物多样性建模”方法，我们证明了在足够的空间分辨率下进行生物多样性制图的可行性，以便为当地管理决策提供信息，同时也具有足够的效率，可以扩展到数千平方公里。本文是主题为“迈向全球昆虫生物多样性监测工具包”的特刊的一部分。
