There is increasing evidence that intense fishing pressure is not only depleting fish stocks but also causing evolutionary changes to fish populations. In particular, body size and fecundity in wild fish populations may be altered in response to the high and often size‐selective mortality exerted by fisheries. While these effects can have serious consequences for the viability of fish populations, there are also a range of traits not directly related to body size which could also affect susceptibility to capture by fishing gears—and therefore fisheries‐induced evolution (FIE)—but which have to date been ignored. For example, overlooked within the context of FIE is the likelihood that variation in physiological traits could make some individuals within species more vulnerable to capture. Specifically, traits related to energy balance (e.g., metabolic rate), swimming performance (e.g., aerobic scope), neuroendocrinology (e.g., stress responsiveness) and sensory physiology (e.g., visual acuity) are especially likely to influence vulnerability to capture through a variety of mechanisms. Selection on these traits could produce major shifts in the physiological traits within populations in response to fishing pressure that are yet to be considered but which could influence population resource requirements, resilience, species’ distributions and responses to environmental change.

Animal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is properly addressed. Historically, coarsely-sampled movement data have proved invaluable for understanding large scale processes (e.g., home range, habitat selection, etc.), but modern fine-scale data promise to unlock far more ecological information. While location error can often be ignored in coarsely sampled data, fine-scale data require much more care, and tools to do this have been lacking. Current approaches to dealing with location error largely fall into two categories—either discarding the least accurate location estimates prior to analysis or simultaneously fitting movement and error parameters in a hidden-state model. Unfortunately, both of these approaches have serious flaws. Here, we provide a general framework to account for location error in the analysis of animal tracking data, so that their potential can be unlocked. We apply our error-model-selection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices are used to track a wide range of animal species comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. Then, using empirical data on tracked individuals from multiple species, we provide an overview of modern, error-informed movement analyses, including continuous-time path reconstruction, home-range distribution, home-range overlap, speed and distance estimation. Adding to these techniques, we introduce new error-informed estimators for outlier detection and autocorrelation visualization. We furthermore demonstrate how error-informed analyses on calibrated tracking data can be necessary to ensure that estimates are accurate and insensitive to location error, and allow researchers to use all of their data. Because error-induced biases depend on so many factors—sampling schedule, movement characteristics, tracking device, habitat, etc.—differential bias can easily confound biological inference and lead researchers to draw false conclusions.

Acoustic telemetry is an important tool for studying the behaviour of aquatic organisms in the wild.VEMCO high residence (HR) tags and receivers are a recent introduction in the field of acoustic telemetry and can be paired with existing algorithms (e.g. VEMCO positioning system [VPS]) to obtain high‐resolution two‐dimensional positioning data.Here, we present results of the first documented field test of a VPS composed of HR receivers (hereafter, HR‐VPS). We performed a series of stationary and moving trials with HR tags (mean HR transmission period = 1.5 s) to evaluate the precision, accuracy and temporal capabilities of this positioning technology. In addition, we present a sample of data obtained for five European perch Perca fluviatilis implanted with HR tags (mean HR transmission period = 4 s) to illustrate how this technology can estimate the fine‐scale behaviour of aquatic animals.Accuracy and precision estimates (median [5th–95th percentile]) of HR‐VPS positions for all stationary trials were 5.6 m (4.2–10.8 m) and 0.1 m (0.02–0.07 m), respectively, and depended on the location of tags within the receiver array. In moving tests, tracks generated by HR‐VPS closely mimicked those produced by a handheld GPS held over the tag, but these differed in location by an average of ≈9 m.We found that estimates of animal speed and distance travelled for perch declined when positional data for acoustically tagged perch were thinned to mimic longer transmission periods. These data also revealed a trade‐off between capturing real nonlinear animal movements and the inclusion of positioning error.Our results suggested that HR‐VPS can provide more representative estimates of movement metrics and offer an advancement for studying fine‐scale movements of aquatic organisms, but high‐precision survey techniques may be needed to test these systems.

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.