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All organisms leave traces of their DNA in the environment they live in. This environmental DNA (eDNA) can be used to detect and monitor single species as well as communities, and potentially be utilized in early warning systems to detect alien species. In this report, we give an overview of eDNA-based methods used to detect and monitor alien species, and give examples of speciesspecific assays designed to identify species that are alien to Norwegian nature or have the potential to establish viable populations in Norway (doorknockers). We emphasize the need for several stages of testing before species-specific assays can be operational, and discuss the importance of including models to assess detection probabilities. Going through standards for sampling and analyses, we suggest a number of minimum requirements for eDNA sampling, laboratory practice, bioinformatics and the use of reference libraries, as well as for reporting results from eDNA studies. The origin and fate of eDNA in different environments can influence its usefulness in detecting and monitoring alien species. We outline factors for terrestrial, freshwater and marine ecosystems and provide examples from six case studies where eDNA has been used to detect and/or monitor alien invasive species: invertebrates and vascular plants in soil of imported ornamental plants, freshwater crayfish and crayfish plague, Gyrodactylus salaris, pink salmon, Canadian pondweed, and American lobster. We provide a decision diagram for detection and monitoring of invasive species starting with early considerations for implementation of a monitoring program, and ending with management decision points depending on detection outcomes at different stages. Finally, we provide some key recommendations for the use of eDNA in assessments of alien and doorknocker species. Bruk av miljø-DNA og DNA-baserte metoder for å vurdere og overvåke fremmede arter og dørstokkarter. The use of eDNA and DNA-based methods to assess and monitor alien and doorknocker species

Hyperspectral imaging (HSI) is a promising technology for environmental monitoring with a lot of undeveloped potential due to the high dimensionality and complexity of the data. If temporal effects are studied, such as in a monitoring context, the analysis becomes more challenging as time is added to the dimensions of space (image coordinates) and wavelengths. We conducted a series of laboratory experiments to investigate the impact of different stressor exposure patterns on the spectrum of the cold water coral Desmophyllum pertusum. 65 coral samples were divided into 12 groups, each group being exposed to different types and levels of particles. Hyperspectral images of the coral samples were collected at four time points from prior to exposure to 6 weeks after exposure. To investigate the relationships between the corals’ spectral signatures and controlled experimental parameters, a new software tool for interactive visual exploration was developed and applied, the HypIX (Hyperspectral Image eXplorer) web tool. HypIX combines principles from exploratory data analysis, information visualization and machine learning-based dimension reduction. This combination enables users to select regions of interest (ROI) in all dimensions (2D space, time point and spectrum) for a flexible integrated inspection. We propose two HypIX workflows to find relationships in time series of hyperspectral datasets, namely morphology-based filtering workflow and embedded driven response analysis workflow. With these HypIX workflows three users identified different temporal and spatial patterns in the spectrum of corals exposed to different particle stressor conditions. Corals exposed to particles tended to have a larger change rate than control corals, which was evident as a shifted spectrum. The responses, however, were not uniform for coral samples undergoing the same exposure treatments, indicating individual tolerance levels. We also observed a good inter-observer agreement between the three HyPIX users, indicating that the proposed workflow can be applied to obtain reproducible HSI analysis results. publishedVersion