Variability of Temperature and Salinity in the Middle Atlantic Bight and Gulf of Maine

Monitoring of the waters of the Middle Atlantic Bight and Gulf of Maine has been conducted by the MARMAP Ships of Opportunity Program since the early 1970's. Presented in this atlas are portrayals of the temporal and spatial patterns of surface and bottom temperature and surface salinity for these areas during the period 1978-1990. These patterns are shown in the form of time-space diagrams for single-year and multiyear (base period) time frames. Each base period figure shows thirteen-year (1978-1990) mean conditions, sample variance in the form of standard deviations of the measured values, and data locations. Each single-year figure displays annual conditions, sampling locations, and departures of annual conditions from the thirteen-year means, expressed as algebraic anomalies and standardized anomalies. (PDF file contains 112 pages.)

Measurement errors in body size of sea scallops (Placopecten magellanicus) and their effect on stock assessment models

Body-size measurement errors are usually ignored in stock assessments, but may be important when body-size data (e.g., from visual sur veys) are imprecise. We used experiments and models to quantify measurement errors and their effects on assessment models for sea scallops (Placopecten magellanicus). Errors in size data obscured modes from strong year classes and increased frequency and size of the largest and smallest sizes, potentially biasing growth, mortality, and biomass estimates. Modeling techniques for errors in age data proved useful for errors in size data. In terms of a goodness of model fit to the assessment data, it was more important to accommodate variance than bias. Models that accommodated size errors fitted size data substantially better. We recommend experimental quantification of errors along with a modeling approach that accommodates measurement errors because a direct algebraic approach was not robust and because error parameters were diff icult to estimate in our assessment model. The importance of measurement errors depends on many factors and should be evaluated on a case by case basis.