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

Abstract

ABSTRACTArthropods contribute importantly to ecosystem functioning but remain understudied. This un-dermines 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 com-munity 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 arthropod species across a 225 km2temperate-zone forested landscape. We combine the maps to visu-alise 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. By this ‘sideways biodiversity modelling’, we demonstrate the feasibility of biodiversity mapping with sufficient spatial resolution to inform local management choices, while also being efficient enough to scale up to thousands of square kilometres.