Dataset Information

The Iconic Atlantic Goliath Grouper (Epinephelus itajara): A Comprehensive Assessment of Health Indices in the Southeastern United States Population.

ABSTRACT: The Atlantic Goliath Grouper (Epinephelus itajara) population has rebounded from near extinction to an international status as vulnerable due in part to regional species recovery efforts. The southeastern US population has been recovering with the main spawning locations off the coasts of Florida. Despite their economic importance to the catch-and-release fishery and the dive industry, and their ecological importance as ecosystem engineers resulting in positive impacts on reefs and species richness, baseline health assessment information is very limited in this species to date. The objectives of this study were to: (1) establish reference intervals for hematological and plasma biochemical analytes, and report immune function, oxidative stress, and vitellogenin in mature males and females; (2) evaluate total length, age, and sex in relation to blood analytes in juvenile and mature fish; (3) assess analytes across sampled months in mature male and female fish; and (4) describe the typical light microscopy findings in liver and gill biopsies, including quantitative assessment of pigmented macrophage aggregates. Health indices are reported as reference intervals when applicable, or otherwise descriptively. Blood analyte correlations with length and age, sex differences, and comparisons across months provided relevant physiological considerations, including differences in protein/energy metabolism, tissue growth, sexual maturation, active reproduction, and antigenic stimulation. Liver histology identified changes associated with life stage, active reproduction, or of subclinically to clinically insignificant infectious and/or inflammatory processes. Hepatocellular vacuolation and pigmented macrophage aggregates were prominent. Pigmented macrophage aggregates correlated with total length, presumably from continuous antigenic stimulation and/or metabolic changes as fish grow. Gill histological findings were subtle. The data presented herein provide an essential baseline assessment of a suite of health variables in an iconic marine teleost species, serves as a springboard for future studies relevant to conservation physiology, and allows for population-level applications for conservation management and policy.

SUBMITTER: Malinowski CR

PROVIDER: S-EPMC7546827 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

The Iconic Atlantic Goliath Grouper (<i>Epinephelus itajara</i>): A Comprehensive Assessment of Health Indices in the Southeastern United States Population.

Malinowski Christopher R CR Perrault Justin R JR Coleman Felicia C FC Koenig Christopher C CC Stilwell Justin M JM Cray Carolyn C Stacy Nicole I NI

Frontiers in veterinary science 20200925

The Atlantic Goliath Grouper (<i>Epinephelus itajara</i>) population has rebounded from near extinction to an international status as vulnerable due in part to regional species recovery efforts. The southeastern US population has been recovering with the main spawning locations off the coasts of Florida. Despite their economic importance to the catch-and-release fishery and the dive industry, and their ecological importance as ecosystem engineers resulting in positive impacts on reefs and specie ...[more]

PMID: 33102560

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Project description: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.

Project description:There is broad interest in the development of efficient marine protected areas (MPAs) to reduce bycatch and end overfishing of speckled hind (Epinephelus drummondhayi) and warsaw grouper (Hyporthodus nigritus) in the Atlantic Ocean off the southeastern U.S. We assimilated decades of data from many fishery-dependent, fishery-independent, and anecdotal sources to describe the spatial distribution of these data limited stocks. A spatial classification model was developed to categorize depth-grids based on the distribution of speckled hind and warsaw grouper point observations and identified benthic habitats. Logistic regression analysis was used to develop a quantitative model to predict the spatial distribution of speckled hind and warsaw grouper as a function of depth, latitude, and habitat. Models, controlling for sampling gear effects, were selected based on AIC and 10-fold cross validation. The best-fitting model for warsaw grouper included latitude and depth to explain 10.8% of the variability in probability of detection, with a false prediction rate of 28-33%. The best-fitting model for speckled hind, per cross-validation, included latitude and depth to explain 36.8% of the variability in probability of detection, with a false prediction rate of 25-27%. The best-fitting speckled hind model, per AIC, also included habitat, but had false prediction rates up to 36%. Speckled hind and warsaw grouper habitats followed a shelf-edge hardbottom ridge from North Carolina to southeast Florida, with speckled hind more common to the north and warsaw grouper more common to the south. The proportion of habitat classifications and model-estimated stock contained within established and proposed MPAs was computed. Existing MPAs covered 10% of probable shelf-edge habitats for speckled hind and warsaw grouper, protecting 3-8% of speckled hind and 8% of warsaw grouper stocks. Proposed MPAs could add 24% more probable shelf-edge habitat, and protect an additional 14-29% of speckled hind and 20% of warsaw grouper stocks.

Dataset Information

The Iconic Atlantic Goliath Grouper (Epinephelus itajara): A Comprehensive Assessment of Health Indices in the Southeastern United States Population.

Publications

The Iconic Atlantic Goliath Grouper (<i>Epinephelus itajara</i>): A Comprehensive Assessment of Health Indices in the Southeastern United States Population.

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