Adaptive Sampling and Identification of Calanoid Copepods Using Acoustic Sensor Data and eDNA Metabarcoding: A Data-Driven Approach

Abstract

To achieve the aims of the Convention on Biological Diversity’s 2030 Global Biodiversity Framework, the management of marine areas requires the use of cost- and time- effective monitoring of biodiversity. Marine observation platforms are increasingly used for such monitoring activities. These platforms allow data to be collected from a variety of sensors simultaneously in real-time and in situ, providing the opportunity for both adapting where and when sampling is performed and for using multiple data streams for more comprehensive information to be collected on marine biodiversity. While some recent monitoring activities are following an adaptive sampling approach (in which sampling is adapted to ecological phenomena such as diel vertical migration), most still do not use a data-driven approach (in which multiple data streams are used to inform sampling). In this study, we performed eDNA metabarcoding to monitor the community of copepods found in the high-latitude environment near Trondheim, Norway. We applied an adaptive sampling approach to detect calanoid copepods at seasonally contrasting time points using real-time acoustic sensor data. Acoustic sensor data were used to inform when plankton net samples were collected in a 24 h period, as Calanoid copepods migrate through the water column throughout the day to avoid predation. These results demonstrate how multiple information streams from complimentary monitoring methods can be combined for more robust monitoring of biodiversity by confirming the presence (or absence) of relevant taxa.

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