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.

Keywords

• valvometry
• dynamic model
• biological early warning system
• optimisation
• ecophysiology