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A General Bayesian Integrated Population Dynamics Model for Protected Species

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Project Overview
With the increasing popularity of the precautionary approach, restrictions on fishing operations have been increasing in an effort to protect at-risk species. These restrictions are often due to making conservative decisions because there is insufficient information about the effects of bycatch on many protected species. Often these restrictions have large effects on the local economies and the livelihoods of the fishermen. Therefore, it is important to provide quantitative analyses of both the effects of bycatch on these species and the effect of regulations on the fisheries. Due to incomplete knowledge of biological systems, uncertainty is an integral component of the management of protected species and, to be consistent with the precautionary approach, the uncertainty in analyses of these species must be described if the appropriate decisions are to be made. The objective of this proposal is to generate a general Bayesian integrated model for protected species modeling that can be applied to multiple species and used to provide management advice.

Project researchers will develop a general Bayesian integrated model for protected species based on the model developed for the New Zealand sea lion by Hilborn et al. (in prep, see also Maunder 1999; Maunder and Hilborn 2000; Breen et al. in prep). and used, with modifications, for Hector's dolphin (Davies et al. 2001). This spatial and age-structured population dynamics model allows the inclusion of priors for many model parameters and the inclusion of multiple data sets. It includes both compensatory and depensatory mortality in the first year of life. Movement between subpopulations is a function of the population size relative to carrying capacity and distance between subpopulations. The data used in this model are absolute or relative abundance estimates by subpopulation, which can be age-structured or population estimates. The model also has a forward projection component that allows for decision analysis based on human-caused mortality (interaction with fisheries) that includes parameter uncertainty and process error (e.g., stochasticity in recruitment and survival, catastrophes, and regime shifts in carrying capacity). The model will be extended to include tagging data (with multiple recaptures, e.g., banding), catch-at-age/length/stage/sex data, environmental/habitat correlations, sex, structure, and stage structure. Sex and stage-structure are added because the biological behavioral characteristics of protected species often differs between sexes and as the individual progress through different stages. The inclusion of tagging data is important because tagging data is traditionally the main source of information for estimating many biological parameters of protected species. The type of tagging data available for protected species differs from the type of data available for fish populations. Most tagging data for protected species (e.g., banding, sight-resight) includes multiple resighting of the same individual. Project researchers will use the methodology of select software packages (e.g., MARK) combined with the relevant literature (e.g., Lebreton et al., and Maunder 2001b) to develop the tagging component of the integrated model. The management strategies to be modeled may include changes in bycatch rates, fishing effort, spatial or temporal allocation of fishing effort, or habitat levels. Methods to determine the effect of management measures on the associated fishery will be investigated (e.g., Maunder et al. 2000). The model will be programmed using the AD Model Builder (ADMB) software package (™Otter Research) which is ideal to implement such a complex model. ADMB is a set of C++ libraries that aid in the development of non-linear models. ADMB provides formatted reading and writing of data structures, matrix algebra, a sophisticated and efficient minimization routine based on automatic derivative calculations and provides the Hessian matrix, and a Markov Chain Monte Carlo (MCMC) routine for sampling from the Bayesian posterior distribution. ADMB has been used to develop a general spatial and age-structured models for fisheries stock assessment, marine mammals, and a general age-structured catch-at-length analysis for tuna stocks. At the core of the ADMB is the AUTODIF library that is used for the MULTIFAN-CL stock assessment model (Fournier et al., 1998). Both posterior mode (penalized likelihood) estimates and full Bayesian integration will be implemented. The model will be developed as generally as possible to allow its application to many different species and for the investigation of many different assumptions and data sets.

In the first year, the model will be applied to the Hawaiian population of black-footed albatross and the northeastern Pacific stock of spotted dolphin. The types and quality of data available for these two populations differ greatly; so they make good contrast for applying a general model. During the first year project researchers will pursue collaborations with other interested researchers who wish to apply the model to their populations. In the second year the model will be applied to these populations, possibly including other Hawaiian albatrosses, other eastern Pacific Ocean dolphins, and sea turtles.

Year 1 funding for this 2-year project to be awarded in early 2003.

Literature cited:
•Breen, P.A., R. Hilborn, M.N. Maunder, and S. Kim. (In prep.) Modelling the effect of fishery bycatch on Hooker's sea lions in New Zealand. Also presented at the Pelagic Fisheries Research Program Protected Species Modeling Workshop, Honolulu, November 13-14, 2001.
•Davies, N.M., J.R. McKenzie, and D.J. Gilbert, 2001. A stochastic projection model for Hector's dolphin. Final Research Report, CSL 99/3093, Department of Conservation, Wellington, N.Z., 46 pp.
•Fournier, D.A., J. Hampton, and J.R. Sibert, 1998. MULTIFAN-CL: A length-based, age-structured model for fisheries stock assessment, with application to South Pacific albacore, Thunnus alalunga. Can. J. Fish. Aquat. Sci. 55: 2105-2116.
•Hillborn, R., M. Pascual, L. Gerber, F. Gulland, and M.N. Maunder. (In prep). A Bayesian approach to incorporating meta-population dynamics, catastrophes, and depensation into calculations of extinction risk. For Ecological Applications.
•Lebreton, J.D., K.P. Burnham, J. Clobert, and D.R. Anderson, 1992. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecological Monographs, 62: 67-118.
•Maunder, M.N. 1999. Development of an age-structured model for New Zealand sea lion. Contract report for the New Zealand Department of Conservation, 12 pp.
•Maunder, M.N. 2000. Software review: AD Model Builder. American Fisheries Society Computer User Section Newsletter, XIV(2): 10-14.
•Maunder, M.N. 2001. Integrated tagging and catch-at-age analysis (ITCAAN). In Spatial Processes and Management of Fish Populations, edited by G.H. Kruse, N. Bez, A. Booth, M.W. Dorn, S. Hills, R.N. Lipcius, D. Pelletier, C. Roy, S.J. Smith, and D. Witherall, Alaska Sea Grant College Program Report No. AK-SG-01-02, University of Alaska-Fairbanks, pp. 123-146.
•Maunder, M.N., and R. Hillborn. 2000. Hooker sea lion model. Final Research Report, CSL 99/3094, Department of Conservation, Wellington, N.Z.

Principal Investigator:

Dr. Mark Maunder
Inter-American Tropical Tuna Commission
Scripps Institution of Oceanography
8604 La Jolla Shores Drive
La Jolla, CA 92037-1508
Phone (858) 546-7027
FAX (858) 546-7133
email: mmaunder@iattc.org

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This page updated August 7, 2008