Designing the Next Generation of Condition Tracking and Early Warning Systems for Shellfish Aquaculture

Abstract

Early detection of environmental disturbances affecting shellfish stock condition is highly desirable for aquaculture activities. In this article, a new biophysical model-based early warning system (EWS) is described, that assesses bivalve stock condition by diagnosing signs of persistent physiological dysfunctioning. The biophysical model represents valve gape dynamics, controlled by active contractions of the adductor muscle countering the passive action of the hinge ligament; the dynamics combine continuous convergence to a steady-state interspersed with discrete closing events. A null simulation was introduced to describe undisturbed conditions. The diagnostic compares valve gape measurements and simulations. Indicators are inferred from the model parameters, and disturbances are assessed when their estimates deviate from their null distribution. Instead of focusing only on discrete events, our EWS exploits the complete observed dynamics within successive time intervals defined by the variation scales. When applied to a valvometry data series, collected in controlled conditions from scallops (Pecten maximus), the EWS indicated that one among four individuals exhibited signs its physiological condition was degrading. This was detected neither during experiments nor during the initial data analysis, suggesting the utility of an approach that quantifies physiological mechanisms underlying functional responses. Practical implementations of biological-EWS at farming sites are then discussed.

Use of Local Ecological Knowledge on the Natural Recruitment of Bivalve Species of Commercial Exploitation in a Natura Area

“…Bivalve fishing is a difficult task for fishers because existing hygienic safety standards exhibited high costs and complicated handling procedures [46]. The latter includes sampling for continuous water and bivalve quality monitoring, veterinary health inspections and documentation for each batch, and packing at a dispatch center prior to distribution [11,47].…”

Using a clustering algorithm to identify patterns of valve-gaping behaviour in mussels reared under different environmental conditions

“…These may be inferred by combining more mechanistic models with the empirical laboratory experiments (e.g. Guarini et al, 2021). Given the availability of sensors and the fast development of technology, having big-data on valve gaping from a few sentinel organisms together with temperature and chlorophyll, becomes more and more feasible these days.…”

Modelling the Dynamics of Outbreak Species: The Case of Ditrupa arietina (O.F. Müller), Gulf of Lions, NW Mediterranean Sea

“…One way around this would be to implement formal forecasting methods [69]. This can be achieved using advanced data assimilation or optimal control techniques in a formalised, quantitative reasoning framework linking physiological and environmental parameters (e.g., [70,71]). This, however, requires access to long, continuous data series, including for the environmental variables hypothesised to influence the outbreaks.…”