Age and growth estimates for the nurse shark (Ginglymostoma cirratum) over 17 years in Bimini, The Bahamas

Age and growth estimates are essential for life history modeling in elasmobranchs and are used to inform accurate conservation and management decisions. The nurse shark (Ginglymostoma cirratum) is abundant in coastal waters of the Atlantic Ocean, yet many aspects of their life history remain relatively understudied, aside from their reproductive behavior. We used mark-recapture data of 91 individual G. cirratum from Bimini, The Bahamas, from 2003 to 2020, to calculate von Bertalanffy (vB) growth parameters, empirical growth rate, and age derived from the resulting length-at-age estimates. The Fabens method for estimating growth from mark-recapture methods was applied through a Bayesian framework using Markov chain Monte Carlo (MCMC) methods. This provided growth parameters with an asymptotic total length (L∞) of 303.28 cm and a growth coefficient (k) of 0.04 yr-1. The average growth rate for G. cirratum was approximately 8.68 ± 6.00 cm yr-1. This study also suggests that the previous maximum age for G. cirratum is likely underestimated, with the oldest individual predicted to be 43 years old. Our study is the first to present vB growth parameters and a growth curve for G. cirratum. It indicates that this species is slow-growing and long-lived, which improves our understanding of their life history

First insights into the vertical habitat use of the whitespotted eagle ray Aetobatus narinari revealed by pop‐up satellite archival tags

The whitespotted eagle ray Aetobatus narinari is a tropical to warm‐temperate benthopelagic batoid that ranges widely throughout the western Atlantic Ocean. Despite conservation concerns for the species, its vertical habitat use and diving behaviour remain unknown. Patterns and drivers in the depth distribution of A. narinari were investigated at two separate locations, the western North Atlantic (Islands of Bermuda) and the eastern Gulf of Mexico (Sarasota, Florida, U.S.A.). Between 2010 and 2014, seven pop‐up satellite archival tags were attached to A. narinari using three methods: a through‐tail suture, an external tail‐band and through‐wing attachment. Retention time ranged from 0 to 180 days, with tags attached via the through‐tail method retained longest. Tagged rays spent the majority of time (82.85 ± 12.17% S.D.) within the upper 10 m of the water column and, with one exception, no rays travelled deeper than ~26 m. One Bermuda ray recorded a maximum depth of 50.5 m, suggesting that these animals make excursions off the fore‐reef slope of the Bermuda Platform. Individuals occupied deeper depths (7.42 ± 3.99 m S.D.) during the day versus night (4.90 ± 2.89 m S.D.), which may be explained by foraging and/or predator avoidance. Each individual experienced a significant difference in depth and temperature distributions over the diel cycle. There was evidence that mean hourly depth was best described by location and individual variation using a generalized additive mixed model approach. This is the first study to compare depth distributions of A. narinari from different locations and describe the thermal habitat for this species. Our study highlights the importance of region in describing A. narinari depth use, which may be relevant when developing management plans, whilst demonstrating that diel patterns appear to hold across individuals

Seasonal Dynamics and Environmental Drivers of Goliath Grouper (<i>Epinephelus itajara</i>) Sound Production

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

Classifying Goliath Grouper (Epinephelus itajara) Behaviors from a Novel, Multi-Sensor Tag

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

Visitation patterns of two ray mesopredators at shellfish aquaculture leases in the Indian River Lagoon, Florida.

The Indian River Lagoon is a primary location of field-based "grow-out" for bivalve shellfish aquaculture along Florida's Atlantic coast. Grow-out locations have substantially higher clam densities than surrounding ambient sediment, potentially attracting mollusk predators to the area. Inspired by clammer reports of damaged grow-out gear, we used passive acoustic telemetry to examine the potential interactions between two highly mobile invertivores-whitespotted eagle rays (Aetobatus narinari) and cownose rays (Rhinoptera spp.)-and two clam lease sites in Sebastian, FL and compared these to nearby reference sites (Saint Sebastian River mouth, Sebastian Inlet) from 01 June 2017 to 31 May 2019. Clam lease detections accounted for 11.3% and 5.6% of total detections within the study period, for cownose and whitespotted eagle rays, respectively. Overall, the inlet sites logged the highest proportion of detections for whitespotted eagle rays (85.6%), while cownose rays (11.1%) did not use the inlet region extensively. However, both species had significantly more detections at the inlet receivers during the day, and on the lagoon receivers during the night. Both species exhibited long duration visits (> 17.1 min) to clam lease sites, with the longest visit being 387.5 min. These visit durations did not vary substantially between species, although there was individual variability. Based on generalized additive mixed models, longer visits were observed around 1000 and 1800 h for cownose and whitespotted eagle rays, respectively. Since 84% of all visits were from whitespotted eagle rays and these longer visits were significantly longer at night, this information suggests that observed interactions with the clam leases are potentially underestimated, given most clamming operations occur during daytime (i.e., morning). These results justify the need for continued monitoring of mobile invertivores in the region, including additional experimentation to assess behaviors (e.g., foraging) exhibited at the clam lease sites

DataSheet_2_Age and growth estimates for the nurse shark (Ginglymostoma cirratum) over 17 years in Bimini, The Bahamas.csv

Age and growth estimates are essential for life history modeling in elasmobranchs and are used to inform accurate conservation and management decisions. The nurse shark (Ginglymostoma cirratum) is abundant in coastal waters of the Atlantic Ocean, yet many aspects of their life history remain relatively understudied, aside from their reproductive behavior. We used mark-recapture data of 91 individual G. cirratum from Bimini, The Bahamas, from 2003 to 2020, to calculate von Bertalanffy (vB) growth parameters, empirical growth rate, and age derived from the resulting length-at-age estimates. The Fabens method for estimating growth from mark-recapture methods was applied through a Bayesian framework using Markov chain Monte Carlo (MCMC) methods. This provided growth parameters with an asymptotic total length (L∞) of 303.28 cm and a growth coefficient (k) of 0.04 yr-1. The average growth rate for G. cirratum was approximately 8.68 ± 6.00 cm yr-1. This study also suggests that the previous maximum age for G. cirratum is likely underestimated, with the oldest individual predicted to be 43 years old. Our study is the first to present vB growth parameters and a growth curve for G. cirratum. It indicates that this species is slow-growing and long-lived, which improves our understanding of their life history.</p

DataSheet_1_Age and growth estimates for the nurse shark (Ginglymostoma cirratum) over 17 years in Bimini, The Bahamas.docx

Age and growth estimates are essential for life history modeling in elasmobranchs and are used to inform accurate conservation and management decisions. The nurse shark (Ginglymostoma cirratum) is abundant in coastal waters of the Atlantic Ocean, yet many aspects of their life history remain relatively understudied, aside from their reproductive behavior. We used mark-recapture data of 91 individual G. cirratum from Bimini, The Bahamas, from 2003 to 2020, to calculate von Bertalanffy (vB) growth parameters, empirical growth rate, and age derived from the resulting length-at-age estimates. The Fabens method for estimating growth from mark-recapture methods was applied through a Bayesian framework using Markov chain Monte Carlo (MCMC) methods. This provided growth parameters with an asymptotic total length (L∞) of 303.28 cm and a growth coefficient (k) of 0.04 yr-1. The average growth rate for G. cirratum was approximately 8.68 ± 6.00 cm yr-1. This study also suggests that the previous maximum age for G. cirratum is likely underestimated, with the oldest individual predicted to be 43 years old. Our study is the first to present vB growth parameters and a growth curve for G. cirratum. It indicates that this species is slow-growing and long-lived, which improves our understanding of their life history.</p