Program, Project, or Expedition Name:
Age and Growth of Lane Snapper (Lutjanus synagris) from the Southeast United States and Implications for Management
Data Link: https://github.com/atw15/Lane-Snapper-Growth-Analysis
| Institution | LMRCSC, UM-RSMAS |
| Principal Investigator | Adrianne Wilson | adriannew92@gmail.com | 773-630-5843 |
| Funding Agency/Contract # | NOAA EPP, LMRCSC, NA16SEC4810007 |
| Collection Start Date | 1/1/2015 |
| Objectives of Data Collection Effort | The objectives of this study were to expand the sampling range by collecting sagittal otoliths from fishery-dependent and independent sources, use multiple gear types and statistical methods to minimize biases due to size-selective sampling, and to obtain growth parameters that are more representative of the current lane snapper stock. |
| Collection End Date | 4/1/2022 |
| Data Collectors Identifying Numbers | N/A |
| Ship or Other Platform Name | Data was collected from both fisheries-independent and dependent sources, including contributions from the National Oceanic and Atmospheric Administration, the Southeast Area Monitoring and Assessment Program, the Florida Fish and Wildlife Research Institute, and the University of North Carolina-Chapel Hill. The sources comprised commercial and recreational fisheries as well as fishery-independent surveys. |
| Geographic Location [Latitude(s)/longitude(s)] | United States Gulf of Mexico [[25.2493, -82.4475], [34.6943, -76.6093], [28.0640, -83.5553], [25.3555, -81.5920], [25.7461, -80.1815], [27.3096, -83.4380], [25.5137, -81.5920], [25.4249, -81.3145], [25.3087, -82.1913], [25.0206, -82.3530], [25.0420, -82.2059], [25.0913, -82.3617], [25.1540, -82.5701], [27.5001, -83.4413], [25.1358, -82.0305], [24.5807, -82.1716], [25.0085, -82.3920], [29.3013, -94.7977], [24.6559, -81.2731], [25.7431, -80.0895], [25.9397, -81.7075], [26.4558, -82.2400], [26.1043, -81.5691], [27.1196, -82.3921], [26.3016, -82.1829], [26.1551, -82.0271], [26.0112, -82.0801], [26.1727, -82.5801], [25.0373, -82.2874], [25.1291, -82.1725], [25.1438, -82.1177], [25.2493, -82.4475], [24.5367, -82.4120], [25.5892, -82.3091]] |
| Units | age(days, years), lengths(mm, cm, in), whole weight(g), depth(m), |
| Data Parameters | Types of Data (numerical and categorical) |
| Precision | Numerical floating-point (~7 decimal digits), temporal (hours/minutes) |
| Observation Methodology | Otoliths were processed and aged following the methodology of Vanderkooy et al. (2020). The left otolith, or the right if the left was broken or chipped, was processed with a high-speed thin sectioning machine using the methods of Cowan Jr. et al. (1995). Only otoliths that were more than 90% intact were used for analysis. Transverse cuts were made through the otolith core to a thickness of about 0.5 mm. |
| Instrument/Gear Identification of Description | Otolith processing: high-speed thin sectioning machine, sand paper. Fishing Gear: included trawl, long-line, handline, and trap. |
| Analysis Methodology | The von Bertalanffy growth model was fitted to fork length at fractional age. Growth curves were calculated in R (R Core Team 2023). . Parameters were estimated by non-linear regression, assuming normally distributed errors. Residual plots were used to assess model assumptions. Both normal and lognormal errors were tried. Preliminary growth models were used to estimate growth parameters and eight exploratory models were used to compare von Bertalanffy growth function (VBGF) between groupings. Followed Following the protocol outlined by Derek Ogle (2016). Akaike information criterion (AIC) was also used to evaluate all the models. Bayesian models were used to re-estimate growth parameters. R version 4.2.2 was used for statistical analysis and R-package ‘FSA’ was used for among group comparisons, and model fitting and selection for the non-Bayesian analysis (Ogle et al., 2023; R Core Team, 2022). JAGS version 4.3.1 and R-packages “rjags” and “R2jags” were used for analysis of the Bayesian models (Plummer, 2022). |
| Data Processing/ Reduction Methodology | Age determination (only otloliths 90% intact included, count of annuli, subsampling and second readers for age validation), Data Transformation and cleaning (outlier detection/removal, data normalization), growth parameter estimation (Von Bertalanffy Growth Function, Bayesian truncated normal models, AIC/BIC/DIC model selection). |
| Explanations of Data Quality Flags | Manual Review: Data points were flagged for manual review upon detection of anomalies or uncertainties and second readers were employed to cross-verify the ages of fish. This dual-review process helped minimize errors and discrepancies in age data. The data was extensively explored both statistically and visually to identify outliers and missing values. Data points identified as outliers or with significant gaps were flagged for further investigation. |
| Citations of Relevant Publications and Grey Literature | Wilson, A. T. (2023). Age, Growth, and Genetic Diversity of Lane Snapper, a Data Limited Species (Doctoral dissertation). University of Miami. Available at: https://scholarship.miami.edu/esploro/outputs/doctoral/Age-Growth-and-Genetic-Diversity-of/991031954518002976 |
