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National Center for Ecological Analysis and Synthesis

Project Description

AD Model Builder (ADMB) is a tool for developing integrated statistical models of complex systems. The principle advantages of the ADMB software suite over other approaches are rapid model development, numerical stability, computational speed, precision of model estimates, and the capacity to accommodate relatively large numbers of parameters and data points. The ADMB software has earned acceptance by researchers working on all aspects of resource management. Population models based on the ADMB software are used to monitor more than 150 different sensitive endangered species and commercially valuable fish stocks around the world. The populations modeled using ADMB include such diverse species as whales, dolphins, sea lions, penguins, albatross, abalone, lobsters, tunas, billfish, sharks, rays, anchovy, and pollock. ADMB applications extend beyond stock assessment. ADMB-based software is used for applications critical to the development of place-based management policies. ADMB is an essential building block of the methods used to reconstruct movements of many species of animals tracked with electronic tags. Spatially resolved populations models treat movement as an integral component of population dynamics and depend on ADMB for estimating movement parameters from data. - ADMB applications are critical to the missions of fishery management agencies in the United States and abroad. Stock assessments for commercially important fish stocks and ecologically sensitive protected species around the world depend on ADMB. In the United States, every NOAA Fisheries Science Center uses ADMB in some fashion, and many • commercially important and sustainably managed fisheries depend on ADMB-based stock assessments. These fisheries include, for example, the Gulf of Alaska pollock fishery, which is widely hailed as sustainably managed. The value i of the fisheries dependent on ADMB-based assessments is enormous. The combined landed value of tropical Pacific tunas and the Gulf of Alaska and Bering Sea ground fish alone exceeds US$10 billion. ADMB is also used at Universities and other academic and research institutions. Due to ADMBs wide use in- fisheries assessment and management, it is now taught in courses at 'several universities. The research organizations, government departments, and companies using ADMB, the types of applications being used, and a list of ADMB-based publications are attached to this proposal. (Up-to-date lists can be found on-line at to users than software whose source code remains proprietary. Involvement of the user community in software development harnesses the power of distributed peer review and assures that the software will evolve in directions most useful to its users. Providing the ADMB software at no cost enables users with limited means, such as students and scientists in developing countries, to take advantage of sophisticated state-of-the-art computing algorithms. In addition, the community built around the open source software provides an ideal resource for assisting developing countries in- adopting the technology.

Principal Investigator(s)

John R. Sibert, Mark N. Maunder

Project Dates

Start: December 7, 2007

End: January 7, 2009



Teresa A'mar
National Oceanic and Atmospheric Administration (NOAA)
Johnoel Ancheta
University of Hawaii, Mānoa
Carol A. Blanchette
University of California, Santa Barbara
Sylvain Bonhommeau
University of California, Santa Barbara
Greg A. Breed
University of California, Santa Cruz
Mollie Brooks
McMaster University
Jennifer E. Caselle
University of California, Santa Barbara
Frank Davenport
University of California, Santa Barbara
Trevor D. Davies
Dalhousie University
Kelly L. Decker
NASA Ames Research Center
David A. Fournier
Otter Research Ltd.
Chris Grandin
Fisheries and Oceans Canada
Stephanie E. Hampton
University of California, Santa Barbara
Carrie V. Kappel
University of California, Santa Barbara
Brian P. Kinlan
University of California, Santa Barbara
Tin Klanjscek
University of California, Santa Barbara
Robert Leaf
Virginia Polytechnic Institute and State University
Brian Linton
NOAA, National Marine Fisheries Service (NMFS)
Steven Y. Litvin
Stanford University
Weihai Liu
Michigan State University
Alexander Lowe
University of California, Santa Barbara
Arni Magnusson
Marine Research Institute
Steve J.D. Martell
University of British Columbia
Mark N. Maunder
InterAmerican Tropical Tuna Commission
Duncan N. Menge
University of California, Santa Barbara
Erik B. Muller
Anders Nielsen
University of Hawaii
Mary I. O'Connor
University of California, Santa Barbara
Laure Pecquerie
University of California, Santa Barbara
Christopher R. Perle
Stanford University
Christine Petersen
University of California, Santa Barbara
Jai Ranganathan
University of California, Santa Barbara
James Regetz
University of California, Santa Barbara
Mark P. Schildhauer
University of California, Santa Barbara
Derek Seiple
University of Hawaii
John R. Sibert
University of Hawaii, Mānoa
Tim Sippel
University of Hawaii, Mānoa
Hans J. Skaug
University of Bergen
Ulrich K. Steiner
Stanford University
Crow White
University of California, Santa Barbara
Casper Willestofte Berg
Technical University of Denmark
Arliss Winiship
Dalhousie University


  1. Journal Article / 2012

    AD Model Builder: Using automatic differentiation for statistical inference of highly parameterized complex nonlinear models

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