In recent years, three major innovations have occurred in ecology. (1) The emergence of new statistical methods for analysing community data; (2) the rapid detection of species and whole communities from environmental DNA (eDNA) and bulk-sample DNA; and (3) the wide availability of remotely sensed environmental covariates. The efficiency gains are such that hundreds or even thousands of species can now be detected and, to an extent, quantified in hundreds or even thousands of samples. Collectively, these three innovations have the potential to relieve the problems of data limitation and analysis that environmental management has been struggling with, opening the way to near-real-time tracking of state and change in biodiversity and its functions and services over whole landscapes. The aim of our project is to develop an integrated statistical framework for DNA-based surveys of biodiversity. The framework will allow the estimation of community compositions and the identification of the landscape characteristics that drive them. We will develop a Bayesian hierarchical model accounting for the probabilistic nature of DNA-based data due to observation error and taxonomic uncertainty and for model uncertainty due to the unknown strength and direction of landscape effects on the system. We will build sophisticated and efficient algorithms within a Bayesian framework for identifying the important landscape covariates that predict community structure and provide guidelines on optimal allocation of resources in DNA-based surveys for achieving the required power to infer species distributions and to link them to landscape covariates. The huge potential contribution of DNA-based data to landscape decision-making is demonstrated by how Natural England, Local Planning Authorities, and the NatureSpace Partnership use eDNA to create a biodiversity-offset market ('District Licensing') for the protected Great Crested Newt (GCN). Water samples from 500 ponds across the South Midlands (spanning ~3320 sq km) were tested for GCN and used to create a distribution map, which was then zoned into four 'impact risk' levels. Builders pay a known, sliding-scale fee, and a portion of the fee is used to build and manage new habitat. District Licensing is only feasible with eDNA's greater efficiency. GCN District Licensing expands to at least 16 LPAs in 2020, aiming to go nationwide, which would make it the largest biodiversity-focused, land-use decision scheme in the UK, if not the world. The natural-and highly desirable-extension to the GCN scheme would be to map 'all biodiversity' and to make land-use decisions (e.g. impact risk maps, offset markets, habitat creation) on this broader basis. In fact, samples originally collected for GCN can be repurposed for this larger goal by using 'metabarcoding,' meaning that the eDNA is PCR-amplified for a larger range of taxa. Given the District-Licensing expansion plans, pond eDNA metabarcoding alone could provide an efficient way to map biodiversity across much of the UK. This is far from the only such programme. Ecologists in industry and academia around the world are plunging ahead with large-scale DNA-sampling campaigns, and there is, as yet, no comprehensive set of statistical methods for modelling the individual steps of the new observation processes, quantifying the resulting uncertainty, and assessing how it affects decision-making at the landscape level. Our proposed modelling framework will provide such tools by explicitly capturing measurement bias within biodiversity models as a set of observation processes, and not merely as error. Improving sampling designs and workflows as a result of our proposed models will profoundly increase the efficiency and credibility of inference and therefore reduce the risk of biodiversity loss during the political process of allocating land to different uses.