https://www.selleckchem.com/products/ly-411575.html Juvenile pink salmon were largely robust to elevated CO2 concentrations up to 2000 μatm CO2, with no mortality or change in condition factor over the 2-week exposure duration. After 1 week of exposure, temperature and hypoxia tolerance were significantly reduced in high CO2, an effect that did not persist to 2 weeks of exposure. Haematocrit was increased by 20% after 2 weeks in the CO2 treatments relative to the initial measurements, while plasma [Cl-] was not significantly different. Taken together, these data indicate that juvenile pink salmon are quite resilient to naturally occurring high CO2 levels during their ocean outmigration.Network models are applied across many domains where data can be represented as a network. Two prominent paradigms for modelling networks are statistical models (probabilistic models for the observed network) and mechanistic models (models for network growth and/or evolution). Mechanistic models are better suited for incorporating domain knowledge, to study effects of interventions (such as changes to specific mechanisms) and to forward simulate, but they typically have intractable likelihoods. As such, and in a stark contrast to statistical models, there is a relative dearth of research on model selection for such models despite the otherwise large body of extant work. In this article, we propose a simulator-based procedure for mechanistic network model selection that borrows aspects from Approximate Bayesian Computation along with a means to quantify the uncertainty in the selected model. To select the most suitable network model, we consider and assess the performance of several learning algorithms, most notably the so-called Super Learner, which makes our framework less sensitive to the choice of a particular learning algorithm. Our approach takes advantage of the ease to forward simulate from mechanistic network models to circumvent their intractable likelihoods. The overall proc