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Climate change will increase the frequency and intensity of extreme climatic events (e.g., storms) that result in repeated pulses of hyposalinity in nearshore ecosystems. Sea urchins inhabit these ecosystems and are stenohaline (restricted to salinity levels ∼32‰), thus are particularly susceptible to hyposalinity events. As key benthic omnivores, sea urchins use hydrostatic adhesive tube feet for numerous functions, including attachment to and locomotion on the substratum as they graze for food. Hyposalinity severely impacts sea urchin locomotor and adhesive performance but several ecologically relevant and climate change-related questions remain. First, do sea urchin locomotion and adhesion acclimate to repeated pulses of hyposalinity? Second, how do tube feet respond to tensile forces during single and repeated hyposalinity events? Third, do the negative effects of hyposalinity exposure persist following a return to normal salinity levels? To answer these questions, we repeatedly exposed green sea urchins (Strongylocentrotus droebachiensis) to pulses of three different salinities (control: 32‰, moderate hyposalinity: 22‰, severe hyposalinity: 16‰) over the course of two months and measured locomotor performance, adhesive performance, and tube foot tensile behavior. We also measured these parameters 20 h after sea urchins returned to normal salinity levels. We found no evidence that tube feet performance and properties acclimate to repeated pulses of hyposalinity, at least over the timescale examined in this study. In contrast, hyposalinity has severe consequences on locomotion, adhesion, and tube foot tensile behavior, and these impacts are not limited to the hyposalinity exposure. Our results suggest both moderate and severe hyposalinity events have the potential to increase sea urchin dislodgment and reduce movement, which may impact sea urchin distribution and their role in marine communities.

 

To develop efficient just-in-time personalised treatments, dynamical models are needed that provide a description of how an individual responds to treatment. However, available system identification approaches cannot effectively be applied to most behavioural datasets since, usually, the data collected is subjected to a large amount of noise and time sampling is not uniform. To be able to circumvent these issues, in this paper a new method is proposed for parsimonious system identification of continuous-time systems that does not require specially structured data. The developed algorithm provides an effective way to leverage these ‘non-standard’ datasets to identify continuous time dynamical models that are compati- ble with a-priori information available on the process. The algorithm developed is tested on data obtained from a behavioural study on adolescents and violence. The objective is to model the temporal dynamics of the association between violence exposure and mental health symptoms (depression and anxiety) in day- to-day life among a sample of adolescents at heightened risk for both substance use exposure and problem behaviour. The information extracted from individual models of behaviour such as the maximum burden and the time of fading away of depression/anxiety does differ substantially from person to person. This information has the potential to be useful to design personalised interventions that would have a better chance of succeeding.