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

Freshwater Mussels as Sentinels for Safe Drinking Water Supply in Europe

“…Freshwater mussels are a widely distributed group of relatively long-lived aquatic animals . Because of their sessile filter feeding behavior, benefits provided by mussels have been related to drinking water production. , Freshwater mussels have been also signaled as good sentinels or biomonitors of environmental change, both concerning long-term and acute responses to environmental stressors. , Mussel behavior (e.g., valve gaping) informs about endogenous circadian rhythms, foot extension (foot activity), periods of feeding and respiration, and can be even used to assess exogenous stressful conditions. − Valve gaping behavior can be easily monitored by using high frequency noninvasive (HFNI) valvometers (Figure ), which measure an induced voltage that varies according to the distance between the electromagnetic electrodes. HFNI valvometers are based on the regular gaping of bivalves and the fact that physical (e.g., turbidity) , or chemical (e.g., salinity) stressors disrupt that gaping reference pattern .…”

“… 15 , 16 Mussel behavior (e.g., valve gaping) informs about endogenous circadian rhythms, foot extension (foot activity), periods of feeding and respiration, and can be even used to assess exogenous stressful conditions. 17 − 19 Valve gaping behavior can be easily monitored by using high frequency noninvasive (HFNI) valvometers ( Figure 1 ), which measure an induced voltage that varies according to the distance between the electromagnetic electrodes. HFNI valvometers are based on the regular gaping of bivalves and the fact that physical (e.g., turbidity) 20 , 21 or chemical (e.g., salinity) 16 stressors disrupt that gaping reference pattern.…”

Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms

“…The novelty of the work lies in the application of machine learning algorithms for activity data of bivalve mollusks used as biosensors in the biomonitoring system of water bodies. Previously, Valletta et al [23] and Bertolini et al [24] demonstrated the feasibility of using machine learning algorithms in animal behavior studies, particularly to identify consistent behavioral patterns in the activity of the bivalves Mytilus galloprovincialis and Mytilus edulis. Meyer et al [25] combined statisticalanalysis techniques with machine learning approaches to study the prediction of the movement patterns of ruminants.…”