Spatial management of marine resources requires population dynamic parameters in much greater spatial detail than traditional stock assessments provide. This dissertation presents a suite of methods to improve the spatial prediction of population abundance, fishing and natural mortality and to make greater use of commercial catch data. The main objectives of this dissertation are to determine the efficacy of using the vast amount of data collected by on-board observers on commercial vessels in model-based estimation of abundance and to use the spatial autocorrelation to improve resource mapping and abundance estimation. The first paper presents a methodology for improving variogram estimation when samples exist from multiple years or regions sharing a similar process for generating spatial autocorrelation. In both simulations and in real datasets of oyster abundance the method proposed here reduced the likelihood of failing to obtain a variogram from a set of samples and improved the efficiency of variogram estimation. The second paper presents a simulation of the efficacy of using biased samples for geostatistical predictions. By creating and sampling spatially-autocorrelated datasets in a manner similar to a commercial fishery we found that model-based geostatistics provided a means of obtaining relatively unbiased predictions of abundance using this data. The next paper used catches obtained by onboard observers in the scallop fishery in Georges Bank Closed Area II in 1999 to obtain geostatistical abundance estimates. We used Vessel Monitoring System (VMS) effort data to obtain tows with less than 10% of the total effort. These tows provided geostatistical estimates of initial scallop abundance similar to a preseason fishery-independent survey. Local differences between the observer and survey predictions were driven primarily by data gaps. The last paper obtained spatially-explicit DeLury depletion estimates of dredge efficiency and scallop abundance using VMS data to correct for the actual fished area. Corrected-area efficiency estimates ranged between 20 and 55% with a mean of 45% and maps of abundance closely matched fishery-independent survey estimates. These results indicate that the there is tremendous potential to incorporate commercial fishery data for the purposes of obtaining quantitative resource assessment information.

Five fishery independent data sets were used to investigate multispecies fish recruitment patterns in Chesapeake Bay (1966--1997). Despite differences in sampling gear, sampled habitat, collection methods, and sampling sites, the strongest multispecies recruitment patterns within each data set (revealed by separate principal components analyses) depict a negative relationship between recruitment of spring spawning anadromous fishes and fall-winter continental shelf spawning species. This pattern dominates both low and high frequency components of the multispecies data. Because these two species groups utilize freshwater and oligohaline reaches of the Bay and its tributaries as springtime nursery areas, this Chesapeake Bay Anadromous-Shelf Spawner (CBASS) recruitment pattern was compared to spring climatic variability in the Mid Atlantic region. Using principal components analysis, cluster analysis, and a gridded sea level pressure (SLP) data set, an objective circulation classification technique identified ten synoptic-scale SLP patterns responsible for spring (Mar--May) weather conditions and interannual seasonal climate variability. Classification and regression tree modeling, ordinary least squares, and least trimmed squares regression were used to compare covariability between the CBASS recruitment pattern and the thirty (3 months x 10 patterns) monthly frequency pressure pattern time series. March frequencies of two regional pressure patterns, the Azores-Bermuda and Ohio Valley high pressure systems, were found to account for a large portion of the CBASS pattern's variability. Spring conditions in March, brought on by an early appearance of the Azores-Bermuda High, favor recruitment of shelf spawners while prolonged winter conditions, brought on by a relative dominance of the Ohio Valley high, favor anadromous spawning success. These observations are supported by an analysis of March temperature and precipitation anomaly patterns for the continental U.S. Analyses of hydro-climatic, species specific zooplankton density, and juvenile fish abundance variables for three Bay tributaries demonstrate that the timing of the winter--spring transition differentially influences nursery area habitat suitability in a pattern consistent with the climate-CBASS recruitment relationships described in this study. The climate-recruitment relationships described in this study represent a multivariate variant of Cushing's Match-Mismatch hypothesis.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.