Combining environmental DNA and remote sensing for efficient, fine-scale mapping of arthropod biodiversity

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 within a joint species distribution model. With this approach, we generate distribution maps for 76 arthro-pod species across a 225 km2 temperate-zone forested landscape. We combine the maps to visualise 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.