Open AccessThe risks of using molecular biodiversity data for incidental detection of species of concern
Author(s) -
Darling John A.,
Pochon Xavier,
Abbott Cathryn L.,
Inglis Graeme J.,
Zaiko Anastasija
Publication year - 2020
Publication title -
diversity and distributions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.918
H-Index - 118
eISSN - 1472-4642
pISSN - 1366-9516
DOI - 10.1111/ddi.13108
Subject(s) - biosecurity , biodiversity , threatened species , interim , identification (biology) , data quality , environmental resource management , quality (philosophy) , environmental planning , business , risk analysis (engineering) , ecology , biology , geography , environmental science , metric (unit) , philosophy , archaeology , epistemology , marketing , habitat
Incidental detection of species of concern (e.g., invasive species, pathogens, threatened and endangered species) during biodiversity assessments based on high‐throughput DNA sequencing holds significant risks in the absence of rigorous, fit‐for‐purpose data quality and reporting standards. Molecular biodiversity data are predominantly collected for ecological studies and thus are generated to common quality assurance standards. However, the detection of certain species of concern in these data would likely elicit interest from end users working in biosecurity or other surveillance contexts (e.g., pathogen detection in health‐related fields), for which more stringent quality control standards are essential to ensure that data are suitable for informing decision‐making and can withstand legal or political challenges. We suggest here that data quality and reporting criteria are urgently needed to enable clear identification of those studies that may be appropriately applied to surveillance contexts. In the interim, more pointed disclaimers on uncertainties associated with the detection and identification of species of concern may be warranted in published studies. This is not only to ensure the utility of molecular biodiversity data for consumers, but also to protect data generators from uncritical and potentially ill‐advised application of their science in decision‐making.