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. Author manuscript; available in PMC: 2018 Apr 20.
Published in final edited form as: Mar Policy. 2017;85:56–64. doi: 10.1016/j.marpol.2017.08.014

Table 1.

Applications of genetic technologies to NIS surveillance objectives within the context of the MSFD. Accessibility to non-specialist is assessed based on currently available technology.

NIS survey objective Most relevant MSFD-related objective Applicable technology Specific strength (s) Accessibility to non-specialist Upstream research needed to increase relevance and accessibility
Validate a specific NIS identification first based on morphology early detection of NIS Single specimen collection
Standard (Sanger) sequencing		 Standard and widely available laboratory method High Improve DNA reference data for many European NIS
Targeted (species-specific) surveys early detection of NIS; Trends in NIS distribution Bulk DNA or eDNA combined with species-specific probes (e.g. qPCR or other targeted approach) Increasingly standardized and available molecular methods; Cost effective for surveying a large number of localities/samples Medium Improve reference data when lacking; standardized sampling protocols; design of sensitive and specific probes; improve models to infer population distribution from survey results
Targeted (taxon- or group- specific inventories (e.g. fish, protists)) Trends in NIS distribution; Impacts to native biodiversity Bulk DNA or eDNA combined with HTS using dedicated (taxa-specific) primers and/or dedicated reference database Cost-effective for processing a large number of samples; potential for broad taxonomic coverage Low Improve reference data; standardization of bioinformatic workflows; improve inferences of relative abundance from HTS data; develop user-friendly tools
Non-targeted global inventories Trends in NIS distribution; Impacts to native biodiversity; broad shifts in ecosystem structure and function Bulk DNA or eDNA combined with HTS using “universal” primers and databases Cost-effective for processing a large number of samples; potential for broad taxonomic coverage; surveillance of non-traditional taxa (e.g. meiofauna or microbial communities) Low Improve reference data; standardization of bioinformatic workflows; improve inferences of relative abundance from HTS data; develop user-friendly tools