Inertial measurement unit sensors (IMU; i.e., accelerometer, gyroscope and magnetometer combinations) are frequently fitted to animals to better understand their activity patterns and energy expenditure. Capable of recording hundreds of data points a second, these sensors can quickly produce large datasets that require methods to automate behavioral classification. Here, we describe behaviors derived from a custom-built multi-sensor bio-logging tag attached to Atlantic Goliath grouper (Epinephelus itajara) within a simulated ecosystem. We then compared the performance of two commonly applied machine learning approaches (random forest and support vector machine) to a deep learning approach (convolutional neural network, or CNN) for classifying IMU data from this tag. CNNs are frequently used to recognize activities from IMU data obtained from humans but are less commonly considered for other animals. Thirteen behavioral classes were identified during ethogram development, nine of which were classified. For the conventional machine learning approaches, 187 summary statistics were extracted from the data, including time and frequency domain features. The CNN was fed absolute values obtained from fast Fourier transformations of the raw tri-axial accelerometer, gyroscope and magnetometer channels, with a frequency resolution of 512 data points. Five metrics were used to assess classifier performance; the deep learning approach performed better across all metrics (Sensitivity = 0.962; Specificity = 0.996; F₁-score = 0.962; Matthew’s Correlation Coefficient = 0.959; Cohen’s Kappa = 0.833) than both conventional machine learning approaches. Generally, the random forest performed better than the support vector machine. In some instances, a conventional learning approach yielded a higher performance metric for particular classes (e.g., the random forest had a F₁-score of 0.971 for backward swimming compared to 0.955 for the CNN). Deep learning approaches could potentially improve behavioral classification from IMU data, beyond that obtained from conventional machine learning methods.

Dynamic body acceleration (DBA), measured through animal-attached tags, has emerged as a powerful method for estimating field metabolic rates of free-ranging individuals. Following respirometry to calibrate oxygen consumption rate (ṀO2) with DBA under controlled conditions, predictive models can be applied to DBA data collected from free-ranging individuals. However, laboratory calibrations are generally performed on a relatively narrow size range of animals, which may introduce biases if predictive models are applied to differently sized individuals in the field. Here, we tested the mass dependence of the ṀO2–DBA relationship to develop an experimental framework for the estimation of field metabolic rates when organisms differ in size. We performed respirometry experiments with individuals spanning one order of magnitude in body mass (1.74–17.15 kg) and used a two-stage modelling process to assess the intraspecific scale dependence of the ṀO2–DBA relationship and incorporate such dependencies into the coefficients of ṀO2 predictive models. The final predictive model showed scale dependence; the slope of the ṀO2–DBA relationship was strongly allometric (M1.55), whereas the intercept term scaled closer to isometry (M1.08). Using bootstrapping and simulations, we evaluated the performance of this coefficient-corrected model against commonly used methods of accounting for mass effects on the ṀO2–DBA relationship and found the lowest error and bias in the coefficient-corrected approach. The strong scale dependence of the ṀO2–DBA relationship indicates that caution must be exercised when models developed using one size class are applied to individuals of different sizes.

Depredation (i.e. partial removal of target catch prior to retrieval) caused by sharks is a prevalent issue affecting several fisheries in the southeastern USA. While US fisheries managers have begun monitoring shark depredation in commercial fisheries, there have been few attempts to quantify these interactions in the recreational fishing sector. To address this knowledge gap, we initiated a citizen-science-based project to provide an in-depth characterization of shark depredation in Florida’s recreational fisheries. This was done via multiple approaches, including social media content analysis, online angler surveys, and cooperative fishery-dependent charters. Across methodologies, snapper-grouper species were the most frequently depredated target species group, and bull and sandbar sharks were the most commonly identified depredating species. Forty-three percent of anglers experienced depredation, and the probability of experiencing depredation ranged from 10% to 60% and varied both regionally and seasonally. In addition, average depredation rates ranged from 31% to 47% and were within the range of reported rates from other recreational fisheries. These results will help build a comprehensive understanding of this human-wildlife conflict and could aid fishery managers in developing management measures to address this fisheries conflict. Furthermore, this study highlights the benefit of incorporating citizen science when addressing complex fisheries issues involving stakeholder conflict.

Objective: Management of most fisheries is currently based on single species, so multispecies interactions often constrain fishing, leading to suboptimal yields or overfishing. Ecosystem-based fisheries management (EBFM) uses a more holistic approach to resource access or allocation and takes advantage of multispecies interactions to spread risk and achieve target yields. Portfolio theory is a commonly applied financial tool that considers covariance between assets in an investment portfolio to reduce the risk of achieving economic return targets. This method can be adapted to EBFM for balancing risk with expected benefits for a portfolio of species’ revenue. Portfolio management considers species interdependencies (covariance in revenue time series), uncertainty, and sustainability constraints. Methods: We demonstrate how to apply economic frontier analysis using publicly available landings and revenue data from commercial fisheries and calculate the risk gap between historic portfolios and the EBFM frontier to assess past fishery performance. We identify data challenges and offer guidance on practical decisions for applying portfolio analysis to derive annual efficient frontiers (trade-offs between revenue risk and return) and demonstrate the sensitivity of frontiers to these data decisions and model parameters. Results: In accordance with previous portfolio analyses, results show that the multispecies portfolio approach outperformed single-species management and there was forgone revenue for the associated risk taken in the single-species approach. Conclusions: These demonstrations as well as guidance on data and analysis are intended to facilitate broader evaluation and application of multispecies fishery management and portfolio theory to fisheries.

The Goliath groupers are known to produce characteristic low frequency vocalizations (“calls”) during spawning aggregations and as part of territorial behavior. Acoustic monitoring for Goliath grouper calls around Florida has historically occurred between July and December to capture the spawning season, with a particular focus on August–November. Because of the unique waveform of the Goliath grouper call, we implemented a noise adaptive matched filter to automatically detect Goliath grouper calls from year-round passive acoustic recordings at two wrecks off Florida’s Gulf of Mexico coast. We investigated diel, temporal and environmental factors that could influence call rates throughout the year. Call rates peaked in August, around 0300 EST and just after the full moon. The Goliath groupers were more vocal when background noise was between 70 and 110 dB re 1 µPa. An additional smaller peak in call rates was identified in May, outside of the typical recording period, suggesting there may be other stimuli besides spawning that are eliciting high sound production in this species. Goliath grouper sound production was present year-round, indicative of consistent communication between individuals outside the spawning season.