Summary1. Mass-specific standard metabolic rate (SMR, or maintenance metabolism) varies greatly among individuals. Metabolism is particularly sensitive to variation in food consumption and growth creating the potential for significant bias in measured SMR for animals that are growing (e.g. juveniles) or of uncertain nutritional status. 2. Consequently, interpreting individual variation in metabolism requires a sound understanding of the potentially confounding role of growth and the relative importance of fixed (genetic) vs. environmental drivers of SMR variation. 3. We review the role of growth in measured SMR variation in juvenile salmonids, with the goals of (i) understanding the contribution of growth (and food consumption) to SMR variation through ontogeny, (ii) understanding the relative contributions of tissue maintenance and biosynthesis (overhead costs of growth) to apparent SMR variation, and (iii) using intrinsic growth effects on SMR to model how alternate life-history strategies may influence growth and measured SMR in juvenile salmonids. 4. SMR measures on juveniles, even when post-absorptive, may be inflated by delayed growthassociated overhead costs, unless juveniles are on a maintenance ration (i.e. not growing). Empirical measurements of apparent SMR in food restricted vs. satiated 2-5 g juvenile salmon demonstrate that estimates may be inflated by as much as 67% due to delayed overhead costs of growth, even when SMR measurements are taken 35 h post-feeding. 5. These results indicate that a substantial component of variation in apparent SMR among juvenile salmonids may be associated with (i) environmentally driven variation in ration (where elevated SMR measurements are an artefact of delayed growth overhead costs), (ii) intrinsic (genetic) or plastic organ-system trade-offs related to increasing investment in metabolically expensive digestive tissue responsible for processing food and (iii) intrinsic (genetic) variation in maximum body size and growth among individuals or life-history types. We suggest that selection for differences in adult body size among resident and anadromous forms leading to differences in juvenile growth trajectories may contribute to both SMR variation and habitat segregation in freshwater, where juveniles with higher growth are constrained to foraging in high velocity habitats to meet their greater consumption needs.

Juveniles of different salmonid species often co-exist along environmental gradients, making them a useful model for identifying dominant trade-off axes and their stability within a biological hierarchy (e.g., from individuals to populations to species). In this perspective, we use multivariate trade-offs among juvenile coho salmon (Oncorhynchus kisutch) and rainbow trout (Oncorhynchus mykiss) as a case study to explore broader-scale patterns of trait association. Multivariate ordination identified a dominant trade-off axis between high growth, consumption, and growth efficiency versus high aerobic scope and active metabolism among individual juvenile rainbow trout and three ecologically divergent rainbow trout populations. This pattern suggests a dominant trade-off between growth and active metabolism among individuals and populations, facilitated by simple developmental controls on increased energy intake (larger digestive tract, potentially more risky foraging behavior). In contrast, the adaptive trade-off differentiating species appears to have shifted to maximizing growth and consumption (rainbow trout) versus maximizing growth efficiency (coho), based on evolved differences in foraging strategy and digestive physiology. The generality of these patterns remains uncertain, but trade-offs related to growth efficiency may be an underappreciated dimension of adaptive differentiation in fishes, and their relationship to digestive strategy and active metabolism warrants further investigation.

Group living is widespread among animals and has a range of positive effects on individual foraging and predator avoidance. For fishes, capture by humans constitutes a major source of mortality, and the ecological effects of group living could carry‐over to harvest scenarios if fish are more likely to interact with fishing gears when in social groups. Furthermore, individual metabolic rate can affect both foraging requirements and social behaviors, and could, therefore, have an additional influence on which fish are most vulnerable to capture by fishing. Here, we studied whether social environment (i.e., social group size) and metabolic rate exert independent or interactive effects on the vulnerability of wild zebrafish (Danio rerio) to capture by a baited passive trap gear. Using video analysis, we observed the tendency for individual fish to enter a deployed trap when in different shoal sizes. Fish in larger groups were more vulnerable to capture than fish tested individually or at smaller group sizes. Specifically, focal fish in larger groups entered traps sooner, spent more total time within the trap, and were more likely to re‐enter the trap after an escape. Contrary to expectations, there was evidence that fish with a higher SMR took longer to enter traps, possibly due to a reduced tendency to follow groupmates or attraction to conspecifics already within the trap. Overall, however, social influences appeared to largely overwhelm any link between vulnerability and metabolic rate. The results suggest that group behavior, which in a natural predation setting is beneficial for avoiding predators, could be maladaptive under a trap harvest scenario and be an important mediator of which traits are under harvest associated selection.

A brain–computer interface (BCI)-based controller bridges the gap between smart wheelchairs and physically impaired persons with severe conditions. This paper presents the design of a hybrid BCI controller with six classifiers using an electroencephalogram (EEG) headset to detect hand motor imagery (MI) and jaw electromyography (EMG) signals. A BCI controller and semi-autonomous system is developed to control a smart wheelchair in conjunction with its semi-autonomous capabilities. For data acquisition, an openvibe system and a commercial grade EEG headset are used. A multiple common spatial pattern (CSP) filter and Linear discriminant analysis (LDA) classifier system is used to process and classify the user's brain activity. To convert the classifier data into a signal that is compatible with the semi-autonomous wheelchair system, a fuzzy logic controller (FLC) is integrated in LabVIEW. Subjects are trained to use the BCI system and the classifier profiles are optimized for each user and the results are analyzed for this study. The openvibe “Replay” script and recorded training data are used to evaluate the performance of the controller scheme. For each subject, positive, negative, and false-positive executions are recorded. During the initial testing phase, the positive rates for subjects were strong, but false-positive rates were too high to be used. Therefore, the design is iterated by changing the rules of the FLC and configuration of the LabVIEW script. The configuration with the best positive rates for turn executions is chosen where the average positive rate for turning is 0.68 for subject 1 and 0.64 for subject 2.