Relative to a handful of other rockfish species, widow rockfish are a strong candidate for marine reserve protection. To a lesser extent, so are aurora and yellowtail rockfishes. Because of their particular reproductive patterns, darkblotched rockfish and Pacific ocean perch are not. These conclusions are the results of a study funded by the California Department of Fish and Game and are relevant to the state’s Marine Life Protection Act.

The purpose of this study is to use rockfish life history and reproductive dynamics to determine marine reserve locations to protect the species that demonstrate age-related differences in parturition timing or quality of larvae produced. These long-lived species are extremely vulnerable to overfishing because of their slow population growth rates and late ages at reproduction. Several species of California rockfish are currently in overfished status. Previous research by the principal investigator has demonstrated that in a number of nearshore rockfish species, older females spawn earlier in the season and produce larvae with characteristics that are more likely to survive (Berkeley et al. 2004). Evidence of similar age-related patterns in spawning seasonality and progeny quality has been observed in a diverse range of teleost species. Because even moderate rates of fishing rapidly eliminate older fish from the population, the burden of reproduction is shifted to younger and younger fish. Elimination of older age classes would effectively shorten the parturition season and eliminate reproductive output from the early part of the spawning season. As a consequence, the likelihood of larval production matching peak plankton production will be reduced (the match-mismatch hypothesis; Cushing 1969, 1990). Fish species that display these “maternal age effects” are most likely to benefit from the protection offered by marine reserves, where no fishing is allowed and the population ages naturally, creating a higher percentage of older individuals.

The SciDAC2 Visualization and Analytics Center for Enabling Technologies (VACET) began operation on 10/1/2006. This document, dated 11/27/2006, is the first version of the VACET project management plan. It was requested by and delivered to ASCR/DOE. It outlines the Center's accomplishments in the first six weeks of operation along with broad objectives for the upcoming future (12-24 months).

The Visualization and Analytics Center for Enabling Technologies (VACET) focuses on leveraging scientific visualization and analytics software technology as an enabling technology for increasing scientific productivity and insight. Advances in computational technology have resulted in an 'information big bang,' which in turn has created a significant data understanding challenge. This challenge is widely acknowledged to be one of the primary bottlenecks in contemporary science. The vision of VACET is to adapt, extend, create when necessary, and deploy visual data analysis solutions that are responsive to the needs of DOE's computational and experimental scientists. Our center is engineered to be directly responsive to those needs and to deliver solutions for use in DOE's large open computing facilities. The research and development directly target data understanding problems provided by our scientific application stakeholders. VACET draws from a diverse set of visualization technology ranging from production quality applications and application frameworks to state-of-the-art algorithms for visualization, analysis, analytics, data manipulation, and data management.

The focus of this article is on how one group of researchers the DOE SciDAC Visualization and Analytics Center for Enabling Technologies (VACET) is tackling the daunting task of enabling knowledge discovery through visualization and analytics on some of the world s largest and most complex datasets and on some of the world's largest computational platforms. As a Center for Enabling Technology, VACET s mission is the creation of usable, production-quality visualization and knowledge discovery software infrastructure that runs on large, parallel computer systems at DOE's Open Computing facilities and that provides solutions to challenging visual data exploration and knowledge discovery needs of modern science, particularly the DOE science community.