Many criteria for statistical parameter estimation, such as maximum likelihood, are formulated as a nonlinear optimization problem. Automatic Differentiation Model Builder (ADMB) is a programming framework based on automatic differentiation, aimed at highly nonlinear models with a large number of parameters. The benefits of using AD are computational efficiency and high numerical accuracy, both crucial in many practical problems. We describe the basic components and the underlying philosophy of ADMB, with an emphasis on functionality found in no other statistical software. One example of such a feature is the generic implementation of Laplace approximation of high-dimensional integrals for use in latent variable models. We also review the literature in which ADMB has been used, and discuss future development of ADMB as an open source project. Overall, the main advantages of ADMB are flexibility, speed, precision, stability and built-in methods to quantify uncertainty.

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Despite being one of the most common pieces of information used in assessing the status of fish stocks, relative abundance indices based on catch per unit effort (cpue) data are notoriously problematic. Raw cpue is seldom proportional to abundance over a whole exploitation history and an entire geographic range, because numerous factors affect catch rates. One of the most commonly applied fisheries analyses is standardization of cpue data to remove the effect of factors that bias cpue as an index of abundance. Even if cpue is standardized appropriately, the resulting index of relative abundance, in isolation, provides limited information for management advice or about the effect of fishing. In addition, cpue data generally cannot provide information needed to assess and manage communities or ecosystems. We discuss some of the problems associated with the use of cpue data and some methods to assess and provide management advice about fish populations that can help overcome these problems, including integrated stock assessment models, management strategy evaluation, and adaptive management. We also discuss the inappropriateness of using cpue data to evaluate the status of communities. We use tuna stocks in the Pacific Ocean as examples.

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Fisheries have removed at least 50 million tons of tuna and other top-level predators from the Pacific Ocean pelagic ecosystem since 1950, leading to concerns about a catastrophic reduction in population biomass and the collapse of oceanic food chains. We analyzed all available data from Pacific tuna fisheries for 1950-2004 to provide comprehensive estimates of fishery impacts on population biomass and size structure. Current biomass ranges among species from 36 to 91% of the biomass predicted in the absence of fishing, a level consistent with or higher than standard fisheries management targets. Fish larger than 175 centimeters fork length have decreased from 5% to approximately 1% of the total population. The trophic level of the catch has decreased slightly, but there is no detectable decrease in the trophic level of the population. These results indicate substantial, though not catastrophic, impacts of fisheries on these top-level predators and minor impacts on the ecosystem in the Pacific Ocean.