Towards a global Fishing Vessel Ocean Observing Network (FVON): state of the art and future directions

Ocean observations are the foundation of our understanding of ocean processes. Improving these observations has critical implications for our ability to sustainably derive food from the ocean, predict extreme weather events that take a toll on human life, and produce the goods and services that are needed to meet the needs of a vast and growing population. While there have been great leaps forward in sustained operational monitoring of our oceans there are still key data gaps which result in sub-optimal ocean management and policy decisions. The global fishing industry represents a vast opportunity to create a paradigm shift in how ocean data are collected: the spatio-temporal extent of ocean data gaps overlaps significantly with fishers’ activities; fishing vessels are suitable platforms of opportunity to host communications and sensor equipment; and many fishing vessels effectively conduct a depth-profile through the water column in the course of normal fishing activities, representing a powerful subsurface data collection opportunity. Fishing vessel-collected ocean data can complement existing ocean observing networks by enabling the cost-effective collection of vast amounts of subsurface ocean information in data-sparse regions. There is an emerging global network of fishing vessels participating in collaborative efforts to collect oceanographic data accelerated by innovations in enabling technologies. While there are clear opportunities that arise from partnering with fishing vessels, there are also challenges ranging from geographic and cultural differences in fleets, fishing methods and practices, data processing and management for heterogeneous data, as well as long term engagement of the fishers. To advance fishing vessel-based ocean observation on a global scale, the Fishing Vessel Ocean Observing Network (FVON) aims to maximize data value, establish best practices around data collection and management, and facilitate observation uptake. FVON’s ultimate goals are to foster collaborative fishing vessel-based observations, democratize ocean observation, improve ocean predictions and forecasts, promote sustainable fishing, and power a data-driven blue economy

Testing COI primers for ichthyoplankton metabarcoding and their capability to assess local mesozooplankton communities

DNA metabarcoding is particularly helpful for monitoring taxonomically complex communities and hard to identify morphologically, such as several zoo and ichthyoplankton, which contain eggs and larval stages of unknown species. However, the efficiency of metabarcoding in diversity recovery is dependent on the targeted genetic markers and primers employed. In this work, we compared the performance of three different primer pairs from cytochrome oxidase subunit I (COI) genetic marker in species detection from marine mesozooplankton samples and its potential to be implemented in biomonitoring programs. We employed the mlCOIintF/LoboR1 primer combination targeting marine metazoans, and two newly designed fish-specific primer cocktails for targeting the ichthyoplankton. Mesozooplankton samples were collected at 4 locations on the Portuguese coast – 1 in the northwest (Viana do Castelo) and 3 in the south (coastal lagoons of Ria de Alvor and Ria Formosa, and in the river Guadiana estuary). Bulk community DNA was extracted using a non-destructive protocol and amplicon libraries produced for the 3 primers combinations. After quality-filtering bioinformatic steps, we obtained 3.04 x 105 usable sequences, of which 76.26% were clustered into OTUs (operational taxonomic units) and 46.30% were identified at species level - corresponding to 103 taxa from 8 different metazoan Phyla. The most diverse classes were Malacostraca, Actinopterygii, and Copepoda. As expected, the generic primer pair for marine metazoa (mlCOIintF/LoboR1) retrieved a higher number of species (94) compared with the fish-specific primer cocktails (30). Nevertheless, 9 % of the total species were identified exclusively by the cocktails, of which 42% were fish. These results confirmed the potential of metabarcoding as a tool for profiling zooplankton communities and to assess ichthyoplankton diversity. Multiple primers pairs increased species detection from different taxonomic groups, being the protocol optimization for fish-specific primer cocktails, the next step for its implementation in fish stock assessments.