No abstract

Considerable time and money are expended in the pursuit of catching fish with hooks (e.g., handlining, angling, longlining, trolling, drumlining) across the recreational, commercial and subsistence fishing sectors. The fish and other aquatic organisms (e.g., squid) that are captured are not a random sample of the population because external (e.g., turbidity) and underlying internal variables (e.g., morphology) contribute to variation in vulnerability to hooks. Vulnerability is the probability of capture for any given fish in a given location at a given time and mechanistically explains the population‐level catchability coefficient, which is a fundamental and usually time‐varying (i.e., dynamic) variable in fisheries science and stock assessment. The mechanistic drivers of individual vulnerability to capture are thus of interest to fishers by affecting catch rates, but are also of considerable importance to fisheries managers whenever hook‐and‐line‐generated data contribute to stock assessments. In this paper, individual vulnerability to hooks is conceptualized as a dynamic state, in which individual fish switch between vulnerable and invulnerable states as a function of three interdependent key processes: an individual fish's internal state, its encounter with the gear, and the characteristics of the encountered gear. We develop a new conceptual framework of “vulnerability,” summarize the major drivers of fish vulnerability, and conclude that fish vulnerability involves complex processes. To understand vulnerability, a shift to interdisciplinary research and the integration of ecophysiology, fish ecology, fisheries ecology and human movement ecology, facilitated by new technological developments, is required.

Recent advances in tracking systems have revolutionized our ability to study animal movement in the wild. In aquatic environments, high-resolution acoustic telemetry systems make it technically possible to simultaneously monitor large amounts of individuals at unprecedented spatial and temporal resolutions, providing a unique opportunity to study the behaviour and social interactions using a reality mining approach. Despite the potential, high-resolution telemetry systems have had very limited use in coastal marine areas due to the limitations that these environments pose to the transmission of acoustic signals. This study aims at designing and testing a high-resolution acoustic telemetry system to monitor, for the first time, a natural fish population in an open marine area. First, we conducted preliminary range tests and a computer simulation study to identify the optimal design of the telemetry system. Then, we performed a series of stationary and moving tests to characterize the performance of the system in terms of positioning efficiency and precision. Finally, we obtained a dataset corresponding to the movements of 170 concurrently tagged individuals to demonstrate the overall functioning of the system with a real study case of the behaviour of a small-bodied coastal species. Our results show that high-resolution acoustic telemetry systems efficiently generate positional data in marine systems, providing a precision of few meters, a temporal resolution of few seconds, and the possibility of tracking hundreds of individuals simultaneously. Data post-processing using a trajectory filter and movement models proved to be key to achieve a sub-meter positioning precision. The main limitation detected for our system was the restricted detection range, which was negatively affected by the stratification of the water column. Our work demonstrates that high-resolution acoustic telemetry systems are an effective method to monitor the movements of free-ranging individuals at the population level in coastal sites. By providing highly precise positioning estimates of large amounts of individuals, these systems represent a powerful tool to study key ecological processes regarding the social interactions of individuals, including social dynamics, collective movements, or responses to environmental perturbations, and to extend the studies to poorly studied small-sized species or life-stages.

State-space models (SSM) are increasingly applied in studies involving biotelemetry-generated positional data because they are able to estimate movement parameters from positions that are unobserved or have been observed with non-negligible observational error. Popular telemetry systems in marine coastal fish consist of arrays of omnidirectional acoustic receivers, which generate a multivariate time-series of detection events across the tracking period. Here we report a novel Bayesian fitting of a SSM application that couples mechanistic movement properties within a home range (a specific case of random walk weighted by an Ornstein-Uhlenbeck process) with a model of observational error typical for data obtained from acoustic receiver arrays. We explored the performance and accuracy of the approach through simulation modelling and extensive sensitivity analyses of the effects of various configurations of movement properties and time-steps among positions. Model results show an accurate and unbiased estimation of the movement parameters, and in most cases the simulated movement parameters were properly retrieved. Only in extreme situations (when fast swimming speeds are combined with pooling the number of detections over long time-steps) the model produced some bias that needs to be accounted for in field applications. Our method was subsequently applied to real acoustic tracking data collected from a small marine coastal fish species, the pearly razorfish, Xyrichtys novacula. The Bayesian SSM we present here constitutes an alternative for those used to the Bayesian way of reasoning. Our Bayesian SSM can be easily adapted and generalized to any species, thereby allowing studies in freely roaming animals on the ecological and evolutionary consequences of home ranges and territory establishment, both in fishes and in other taxa.