# Journal papers and Formal publications

A short description of how the research benefited by using ADMB is included, when provided by the authors. Articles are linked to available examples, related files and PDFs when available.

By First Author’s Last Name: A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

A’mar, Z.T., Punt, A.E., and Dorn, M.W. 2009. The impact of regime shifts on the performance of management strategies for the Gulf of Alaska walleye pollock (*Theragra chalcogramma*) fishery. Can. J. Fish. Aquat. Sci. 66(12): 2222–2242.

Adam, M. S., and G. P. Kirkwood. 2001. Estimating tag-shedding rates for skipjack tuna, *Katsuwonus pelamis* , off the Maldives. Fish. Bull. 99: 193-196.

Adam, M. S., and J. R. Sibert. 2002. Population dynamics and movements of skipjack tuna (*Katsuwonus pelamis*) in the Maldivian fishery: analysis of tagging data from an advection-diffusion-reaction model. Aquat. Living Res. 15: 13-23.

Aires-da-Silva, A.M., Maunder, M.N. Gallucci, V.F., Kohler, N.E. and Hoey, J.J. 2009. A spatially-structured tagging model to estimate movement and fishing mortality rates for the blue shark (*Prionace glauca*) in the North Atlantic Ocean. Marine and Freshwater Research 60: 1029-1043.

Berger, A.M., M.L. Jones, Y. Zhao, and J.R. Bence. 2012. Accounting for spatial population structure at scales relevant to life history improves stock assessment: the case for Lake Erie walleye *Sander vitreus*. Fisheries Research 115-116:44-59.

Bigelow, K.A., and M.N. Maunder. 2007. Does habitat or depth influence catch rates of pelagic species?, Can. J. Fish. Aqu. Sci. 64:1581−1594.

Booth, A.J. & Quinn II, T.J. 2006. Bayesian and maximum likelihood approaches to stock assessment when abundance indices are questionable. Fisheries Research 80: 169-181. ADMB was used because of the ease of switching between frequentist and Bayesian paradigms.

Booth, A.J. & Potts. 2006. Estimating gill-net selectivity for *Labeo umbratus* (Pisces:Cyprinidae) and an evaluation of using fyke-nets as a non-destructive sampling gear in small reservoirs. Fisheries Research 79: 202-209. ADMB was used as it is straight-forward to estimate standard errors of derived variables.

Booth, A.J. & Weyl, O.L.F. 2008. Retention of T-bar anchor and dart tags by a wild population of African sharptooth catfish, *Clarias gariepinus*. Fisheries Research 92: 333-339. ADMB was used as it is straight-forward to estimate standard errors of derived variables.

Booth, A.J., Traas, G.R.L. & Weyl, O.L.F. 2010. Adult African sharptooth catfish, Clarias gariepinus, population dynamics in a small invaded warm-temperate impoundment. African Zoology 45(2): 299-308. (This is modified CJS mark-recapture model.)

Branch, T. A., E. M. N. Abubaker, S. Mkango, and D. S. Butterworth. 2007. Separating southern blue whale subspecies based on length frequencies of sexually mature females. Marine Mammal Science. 23: 803-833.

Branch, T. A., K. Matsuoka, and T. Miyashita. 2004. Evidence for increases in Antarctic blue whales based on Bayesian modelling. Marine Mammal Science 20:726-754.

Branch, T.A. & Mikhalev, Y.A. 2008. Regional differences in length at sexual maturity for female blue whales based on recovered Soviet whaling data. Marine Mammal Science.

Branch, T.A., Mikhalev, Y.A. & Kato, H. 2009. Separating pygmy and Antarctic blue whales using long-forgotten ovarian data. Marine Mammal Science.

Branch, T. A. and Hilborn, R. 2010. A general model for reconstructing salmon runs. Canadian Journal of Fisheries and Aquatic Sciences, 67(5): 886-904.

Breen, P.A.., Hilborn, R., Maunder, M.N., and Kim, S.W. 2003. Effects of alternative control rules on the conflict between a fishery and a threatened sea lion (*Phocarctos hookeri*). Can. J. Fish. Aquat. Sci. 60: 527-541.

Breen, P.A., S.W. Kim & N.L. Andrew. 2003. A length-based Bayesian stock assessment model for abalone. Marine and Freshwater Research 54(5): 619-634.

Broms, K., Skalski, J.R., Millspaugh, J.J., Hagen, C.A., and Schulz, J.H. (2010) Using Statistical Population Reconstruction to Estimate Demographic Trends in Small Game Populations. Journal of Wildlife Management 74(2):310-317.

Bull, B. and Cordue, P.L. 2000. Sensitivities of fishery performance indicators to the assumptions of a single stock model. New Zealand Fisheries Assessment Report 2000/39. 33 p.

Brenden, T.O., J.R. Bence, and E.B. Szalai. 2012. An age-structured integrated assessment of Chinook salmon population dynamics in Lake Huron’s main basin since 1968. Transactions of the American Fisheries Society 141:919-933. (An integrated assessment application using ADMB)

Brenden, T.O., J.R. Bence, B.F. Lantry, J.R. Lantry, and T. Schaner. 2011. Population dynamics of Lake Ontario lake trout during 1985-2007. North American Journal of Fisheries Management 31:962-979. (Integrated fishery assessment application using ADMB)

Cardinale, M., Hagberg, J., Svedäng, H., Bartolino, V., Gedamke, T., Hjelm, J., and Norén, F. 2010. Fishing through time: population dynamics of plaice (*Pleuronectes platessa*) in the Kattegat–Skagerrak over a century. Population ecology, 52(2), 251-262.

Chen, Y., M. Kanaiwa, and C. Wilson. 2005. Developing a Bayesian stock assessment framework for the American lobster fishery in the Gulf of Maine. New Zealand Journal of Freshwater and Marine Sciences (Special issue on Lobster Biology and Management) 39:645-660.

Collie, J.S. and A.K. DeLong. 1999. Multispecies interactions in the Georges Bank fish community. In, Ecosystem approaches for fisheries management. Alaska Sea Grant College Program, AK-SG-99-01, p 187-210.

Coggins, L. G. J., W. E. I. Pine, C. J. Walters, and S. J. D. Martell. 2006. Age-structured mark-recapture analysis: A virtual-populatoin-analysis-based model for analyzing age-structured capture-recapture data. North American Journal of Fisheries Management 26:201-205.

Conn, P. B., Williams, E. H., and Shertzer, K. W. 2010. When can we reliably estimate the productivity of fish stocks? Canadian Journal of Fisheries and Aquatic Sciences, 67(3): 511-523.

Cooper, A.B., R. Hilborn, and J.W. Unsworth. 2003. An approach for population assessment in the absence of abundance indices. Ecological Applications 13(3): 814-818.

Cope, J. 2013. Implementing a statistical catch-at-age model (Stock Synthesis) as a tool for deriving overfishing limits in data-limited situations, Fisheries Research, 142:3-14.

Cordue, P.L., Coombs, R.F., and Macaulay, G.J. 2001: A least squares method of estimating length to target strength relationships from in situ target strength distributions and length frequencies. J. Acoust. Soc. Am. 109: 155–163.

Cubillos, L.A., Aguayo, M., Neira, M., Sanhueza, E., and Castillo-Jordán, C. 2009. Age verification and growth of the Chilean cardinalfish *Epigonus **crassicaudus* (de Buen, 1959) admitting ageing error. Revista de Biología Marina y Oceanografía 44: 417-427

Cui., G., Bax, N.J., Punt, A.E. and I.A. Knuckey. 2001. Estimating gill-net selectivity for five species caught in the South East Fishery, Australia. Mar. Freshwater Res. 52: 691-699.

Cui, G., Punt, A.E., Pastene, L.A. and M. Goto. 2002. Bayes and Empirical Bayes approaches to addressing stock structure questions using mtDNA data, with an illustrative application to the North Pacific minke whales. J. Cetacean Res. Manage. 4(2): 123-134.

Deriso, R.B., Maunder, M.N., and Skalski, J.R. 2007. Variance estimation in integrated assessment models and its importance for hypothesis testing. Can. J. Fish. Aquat. Sci. 64: 187-197

Deriso, R.B., Maunder, M.N., and Pearson, W.H. 2008. Incorporating covariates into fisheries stock assessment models with application to Pacific herring of Prince William Sound, Alaska. Ecological Applications 18(5): 1270-1286.

Deroba, J.J., and J.R. Bence. 2012. Evaluating harvest control rules for lake whitefish in the Great Lakes: accounting for variable life-history traits. Fisheries Research 121-122:88-103. (Simulation using ADMB as a shell to take advantage of the autodiff and admb libraries. No estimation is involved)

Dichmont, C.M., Deng, A., Punt, A., Venables, W. and Haddon, M. 2006. Management Strategies for short lived species: the case of Australia’s Northern Prawn Fishery. 1. Accounting for multiple species, spatial structure and implementation uncertainty when evaluating risk. Fisheries Research. 82: 204–220.

Dichmont, C.M., Deng, A., Punt, A., Venables, W. and Haddon, M. 2006. Management Strategies for short lived species: the case of Australia’s Northern Prawn Fishery 2. Choosing appropriate management strategies using input controls. Fisheries Research. 82: 221–234.

Dichmont, C.M., Deng, A., Punt, A., Venables, W. and Haddon, M. 2006. Management Strategies for short lived species: the case of Australia’s Northern Prawn Fishery 3. Factors affecting management and estimation performance. Fisheries Research. 82: 235–245.

Dichmont, C.M., Punt, A.E, Deng, A, Dell, Q and Venables, W. 2003. Application of a weekly delay-difference model to commercial catch and effort data for tiger prawns in Australia’s Northern Prawn Fishery. Fisheries Research. 65: 335-350.

Dicken, M.L., Booth, A.J & Smale, M.J. 2008. Estimates of juvenile and adult raggedtooth shark (*Carcharias taurus*) abundance along the east coast of South Africa. Canadian Journal of Fisheries and Aquatic Sciences 65: 621-632. (This is modified CJS mark-recapture model with under-reporting and tag loss included.)

Ebener, M.P., J.R. Bence, K. Newman and P. Schneeberger. 2005. Application of Statistical catch-at-age models to assess lake whitefish stocks in the 1836 treaty-ceded waters of the upper Great Lakes. Pages 271-309 in L.C. Mohr and T.F. Nalepa eds., Proceedings of a workshop on the dynamics of lake whitefish (*Coregonus clupeaformis*) and the amphipod Diporeia spp. in the Great Lakes. Great Lakes Fish. Comm. Tech. Rep. 66.

Edeline, E., Le Rouzic, A., Winfield, I.J., Fletcher, J.M., James, J.B., Stenseth, N.C., and Vøllestad, L.A. 2009. Harvest-induced disruptive selection increases variance in fitness-related traits. Proc. R. Soc. B 276: 4163-4171.

Fenichel, E. P. and Horan, R. D. 2007. Gender-based harvesting in wildlife disease management. American Journal of Agricultural Economics.

Flores, L., Ernst, B., and Parma, A.M. (2010) Growth pattern of the sea urchin, *Loxechinus albus* (Molina, 1782) in southern Chile: evaluation of growth models. Mar Biol 157: 967–977.

Ford, J.H, Bravington, M.V., Robbins, J. 2012. Incorporating individual variability into mark–recapture models. Methods in Ecology and Evolution 3, 1057-1054.

Fournier, D.A., Sibert, J.R., Majkowski, J. and Hampton, J. 1990. MULTIFAN a likelihood-based method for estimating growth parameters and age-composition from multiple length frequency data sets illustrated using data for southern bluefin tuna (*Thunnus maccoyii*). Canadian Journal of Fisheries and Aquatic Science 47,301-317.

Frédoua, T., Ferreira, B.P., Letourneur, Y. 2009. Assessing the stocks of the primary snappers caught in Northeastern Brazilian Reef Systems. 2-A multi-fleet age-structured approach. Fisheries Research, 99: 97-105

Frisk, M. G., Martell, S. J. D., Miller, T. J., and Sosebee, K. 2010. Exploring the population dynamics of winter skate (*Leucoraja ocellata*) in the Georges Bank region using a statistical catch-at-age model incorporating length, migration, and recruitment process errors. Canadian Journal of Fisheries and Aquatic Sciences, 67(5): 774-792.

Gibson, A.J.F. and Myers, R.A. 2003. A statistical, age-structured, life-history-based stock assessment model for anadromous *Alosa*. American Fisheries Society Symposium 35: 275-283.

Haeseker, S., M. Jones, and J. Bence. 2003. Estimating uncertainty in the stock-recruitment relationship for St. Marys River sea lampreys. Journal of Great Lakes Research 29 (Supplement 1): 728-741.

Haist, V. 1998. Integrated catch-age mark-recapture model: application to B.C. sablefish stocks. In: Fishery Stock Assessment Models (Proceedings of the International Symposium on Fishery Stock Assessment Models for the 21st Century, October 8-11, 1997, Anchorage, Alaska) F. Funk, T.J. Quinn II, J. Heifetz, J.N. Ianelli, J.E. Powers, J.J. Schweigert, P.J. Sullivan, and C.I. Zhang eds. Alaska Sea Grant College Program Report No. AK-SG-98-01, University of Alaska Fairbanks, pp. 679-692.

Haist, V., Breen, P.A., and Starr, P.J. 2009. A multi-stock, length-based assessment model for New Zealand rock lobster (*Jasus Edwardsii*). New Zealand Journal of Marine and Freshwater Research, 43(1): 355 – 371.

Hancet, S., V. Haist and D. Fournier. 1998. An integrated assessment of southern blue whiting (*Micromesistius australis*) from New Zealand using separable Sequential Population Analysis. Pages 155-170 in F. Funk, T.J. Quinn II, J. Heifetz, J.N. Ianelli, J.E. Powers, J.F. Schweigert, P.J. Sullivan and C.I. Zhang, editors. Fishery Stock Assessment Models. Alaska Sea Grant College Program Report No. AKSG- 98-01, University of Alaska Fairbanks.

Hart, D.R. and Chute, A.S. (2009) Verification of Atlantic sea scallop (*Placopecten magellanicus*) shell growth rings by tracking cohorts in fishery closed areas Can. J. Fish. Aquat. Sci. 66: 751–758.

Hilborn, R.H., Parma, A., and Maunder, M.N. 2002. Exploitation rate reference points for west coast rockfish: are they robust and are there better alternatives? North American Journal of Fisheries Management, 22: 365-375.

Holland, K.N. and J.R. Sibert. 1994. Physiological thermoregulation in bigeye tuna, Thunnus obesus. Env. Biol. of Fishes 40:319-217.

Holtgrieve, G.W., Schindler, D.E., Branch, T.A., and A’mar, Z.T. 2010. Simultaneous quantification of aquatic ecosystem metabolism and reaeration using a Bayesian statistical model of oxygen dynamics. Limnol. Oceanogr., 55(3): 1047–1063

Hyun, S., Hilborn, R., Anderson, J.J., and Ernst, B. 2005. A statistical model for inseason forecasts of sockeye salmon returns to the Bristol Bay districts of Alaska. CJFAS 62: 1665-1680.

Ichinokawa, M, Coan, A.L., and Takeuchi, Y. 2008. Transoceanic migration rates of young North Pacific albacore, *Thunnus alalunga*, from conventional tagging data. Canadian Journal of Fisheries and Aquatic Sciences, 65(8): 1681-1691.

Irwin, B.J., T. Wagner, J.R. Bence, M. Kepler, W.Liu and D. Hayes. 2013. Estimating spatial and temporal components of variation for fisheries count data using negative binomial mixed models. Transactions of the American Fisheries Society. (Hierarchical mixed-effect models for survey catch data using ADMB-RE)

Jensen, O.P., Gilroy, D.J., Hogan, Z., Allen, B.C., Hrabik, T.R., Weidel, B.C., Chandra, S., and Vander Zanden, M.J. 2009. Evaluating recreational fisheries for an endangered species: a case study of taimen, *Hucho taimen*, in Mongolia Can. J. Fish. Aquat. Sci. 66: 1707–1718

Jurado-Molina, J. Livingston, P.A. and Ianelli, J.N. (2005) Incorporating predation interactions in a statistical catch-at-age model for a predator–prey system in the eastern Bering Sea. Can. J. Fish. Aquat. Sci. 62: 1865–1873

Kanaiwa, M., Y. Chen, and M. Hunter. 2005. An evaluation of a complex length-based fisheries stock assessment model for the green sea urchin fishery in Maine, USA. Fisheries Research 74:96-115.

Kleiber, P. and Hampton, J. 1994. Modelling effects of FADs and islands on movement of skipjack tuna (*Katsuwonus pelamis*): Estimating parameters from tagging data. Can.J.Fish.Aquat.Sci. 51;2642--2653.

Kocovsky, P.M. and Stapanian, M.A. 2010. Night sampling improves indices used for management of yellow perch in Lake Erie. Fisheries Management and Ecology 17: 10–18.

Krag, L. A., Holst, R., and Madsen, N. 2009. The vertical separation of fish in the aft end of a demersal trawl. ICES Journal of Marine Science, 66: 772–777.

Le Rouzic, A., Skaug, H. J., and Hansen, T. F. 2010. Estimating genetic architectures from artificial-selection responses: A random-effect framework. Theoretical Population Biology 77(2): 119-130.

Lehodey, P., I. Senina, J. Sibert, L. Bopp, B. Calmettes, J. Hampton, and R. Murtugudde, 2010. Preliminary forecasts of population trends for Pacific bigeye tuna under the A2 IPCC scenario. Progress in Oceanography, 86: 302-315.

Lessard, R.B, Hilborn, R., and Chasco, B. E. 2008. Escapement goal analysis and stock reconstruction of sockeye salmon populations (*Oncorhynchus nerka*) using life-history models. Canadian Journal of Fisheries and Aquatic Sciences, 65(10): 2269-2278.

Linton, B.C. and Bence, J.R. 2008. Evaluating methods for estimating process and observation error variances in statistical catch-at-age analysis. Fisheries Research, 94(1): 26-35. (Simulation involving integrated age-structured assessments and fitting of the assessment models was done using ADMB)

Liow, L. H., Skaug, H.J., Ergon, T. and Schweder, T. 2010. Global occurrence trajectories of microfossils: environmental volatility and the rise and fall of individual species. Paleobiology 36(2): 224–252

Lunde, A., Melve, K. K., Gjessing, H. K., Skjærven, R. and L. M. Irgens. 2007. Genetic and Environmental Influences on Birth Weight, Birth Length, Head Circumference, and Gestational Age by Use of Population-based Parent-Offspring Data Am. J. Epidemiol. (2007) 165(7): 734-741. (Used ADMB to model fetal and genetic effects and shared sibling environmental effects on birth weight and gestational age using path analysis and mixed linear models)

Lynch, P.D., K.W. Shertzer, and R.J. Latour. 2012. Performance of methods used to estimate indices of abundance for highly migratory species. Fisheries Research 125-126: 27-39. (ADMB was used to implement the statHBS approach to estimating indices of abundance. ADMB drastically increased the efficiency of these analyses)

MacCall, A. D. 2013. Use of the delta method to evaluate the precision of assessments that fix parameter values. Fisheries Research. 142:56-60.

Magnusson, A. and R. Hilborn. 2007. What makes fisheries data informative? Fish and Fisheries 8:337-358.

Martell, S. J. D., Pine, W. E., and Walters, C. J. 2008. Parameterizing age-structured models from a fisheries management perspective. Can. J. Fish. Aquat. Sci., 65:1586–1600. (ADMB was used to estimate parameters for an age-structured model where the leading parameters consisted of MSY and FMSY)

Martell, S., Walters, C., and Hilborn, R. 2008. Retrospective analysis of harvest management performance for Bristol Bay and Fraser River sockeye salmon (*Oncorhynchus nerka*). Canadian Journal of Fisheries and Aquatic Sciences, 65(3):409–424.

Marty, G.D., Hulson, P-J. F., Miller, S.E., Quinn II, T.J., Moffitt, S.D., and, Merizon, R.A. 2010. Failure of population recovery in relation to disease in Pacific herring. Diseases of Aquatic Organisms 90: 1–14.

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. Witherell, Alaska Sea Grant College Program Report No. AK-SG-01-02, University of Alaska Fairbanks, pp. 123-146.

Maunder M.N. 2001. A general framework for integrating the standardization of catchper- unit-of-effort into stock assessment models. Can. J. Fish. Aquat. Sci., 58: 795- 803.

Maunder, M.N. 2002. Growth of skipjack tuna (*Katsuwonus pelamis*) in the eastern Pacific Ocean, as estimated from tagging data. Inter-American Tropical Tuna Commission Bulletin, 22(2): 93-131.

Maunder, M.N. 2003. Paradigm shifts in fisheries stock assessment: from integrated analysis to Bayesian analysis and back again. Natural Resource Modeling, 16(4): 465-475.

Maunder, M.N. and A.E. Punt. 2013. A review of integrated analysis in fisheries stock assessment. Fisheries Research 142:61-74.

Maunder, M.N. and Starr, P.J. 2001. Bayesian Assessment of the SNA1 snapper (*Pagrus auratus*) stock on the northeast coast of New Zealand. New Zealand Journal of Marine and Freshwater Research, 35: 87-110.

Maunder, M.N. and Deriso, R.B. 2004. Estimation of recruitment in catch-at-age models. Can. J. Fish. Aquat. Sci. 60: 1204-1216.

Maunder, M.N. and Langley, A.D. 2004. Integrating the standardization of catch-per-unit-of- effort into stock assessment models: testing a population dynamics model and using multiple data types. Fisheries Research 70(2-3): 389-395.

Maunder, M.N. and Hinton, M.G. 2006. Estimating relative abundance from catch and effort data, using neural networks. Inter-American Tropical Tunna Commission Special Report 15. pp. 19.

Maunder, M.N., Skaug, H.J., Fournier, D.A., and Hoyle, S.D. 2008. Comparison of estimators for mark-recapture models: random effects, hierarchical Bayes, and AD Model Builder. In: Modeling Demographic Processes in Marked Populations. Eds. Thomson, D.L., Cooch, E.G., and Conroy, M.J. Environmental and Ecological Statistics 3: 917-948.

Maunder M.N., Schnute, J.T., and Ianelli, J. 2009. Computers in Fisheries Population Dynamics. In Megrey, B. and Moksness, E. eds. Computers in Fisheries Research. Kluwer Academic Publishers.

Maunder, M.N. and Deriso, R.B. 2009. Dealing with missing covariate data in fishery stock assessment models. Fisheries Research, 101: 80-86.

McGarvey, R., Feenstra, J.E., and Ye, Q. 2007. Modeling fish numbers dynamically by age and length: partitioning cohorts into "slices". Can. J. Fish. Aquat. Sci. 64: 1157-1173.

McGarvey, R. and Linnane, A. 2009. Estimating historical commercial rock lobster (*Jasus edwardsii*) catch inside Australian State territorial waters for marine protected area assessment: the binomial likelihood method. Biodivers Conserv 18: 1403–1412

Meyey, R. Fournier, D., and Berg, A. 2003. Stochastic Volatility: Bayesian Computation Using Automatic Differentiation and the Extended Kalman Filter. The Econometrics Journal, 6: 408-420.

Methot, R.D. Jr. and C.R. Wetzel. 2013. Stock synthesis: A biological and statistical framework for fish stock assessment and fishery management. Fisheries Research 142: 86-99.

Meyer, R., D.A. Fournier, and A. Berg. 2003. Stochastic volatility: Bayesian computation using automatic differentiation and the extended Kalman filter. Econometrics Journal 6, 408:420. (Used ADMB to model pound/dollar exchange rates using stochastic volatility models)

Millar, R. B, and R. D. Methot. 2002.Age structured meta-analysis of U. S. West Coast rockfish populations and hierarchical modeling of trawl survey catchabilities. Can. J. Fish. Aquat. Sci. 59: 383-392.

Millar, R. B. 2004. Simulated maximum likelihood applied to non-gausian and nonlinear mixed effects and state-space models. Aust. New Zealand J. Stat. 46: 543–554

Minte-Vera, C.V., Branch, T. A., Stewart, I. J., and Dorn, M.W. 2005. Practical application of meta-analysis results: avoiding the double use of data. Canadian Journal of Aquatic and Fisheries Sciences 62: 925-929. DOI: 10.1139/f04-245

Molton, K.J., T.O. Brenden, and J.R. Bence. 2012. Control rule performance for intermixing lake whitefish populations in the 1836 Treaty waters of the Great Lakes: a simulation-based evaluation. Journal of Great Lakes Research 38:686-698. (Simulation study where integrated age-structured assessments are fit at 3-year time intervals. ADMB used both in simulations and estimation, by having one ADMB program call another)

Montenegro, C., Maunder, M.N., and Zilleruelo, M. 2009. Improving management advice through spatially explicit models and sharing information. Fisheries Research 100: 191–199.

Nielsen, A., and Lewy, P. 2002. Comparison of the frequentist properties of Bayes and the maximum likelihood estimators in an age-structured fish stock assessment model. Can. J. Fish. Aquat. Sci. 59(1):136-143.

Olea, P.P. and Mateo-Tomás, P. 2009. The role of traditional farming practices in ecosystem conservation: The case of transhumance and vultures. Biological Conservation, 142: 1844-1853.

Parma, A.M. 2001. Bayesian approaches to the analysis of uncertainty in the stock assessment of pacific halibut. American Fisheries Society Symposium 24, 111-132.

Parma, A.M. 2001. In search of robust harvest rules for Pacific halibut. In the face of uncertain assessments and decadal changes in productivity. Bulletin of Marine Science.

Paulsen, J., Lunde, A., Skaug, H.J. 2008. Fitting mixed-effects models when data are left truncated. Insurance: Mathematics and Economics **43**:** **121-133. (Used ADMB to model insurance damages using a linear mixed model with left truncated data)

Prichard, C.G., and J.R. Bence. 2013. Estimating wounding of lake trout by sea lamprey in the upper Great Lakes: allowing for changing size-specific patterns. Journal of Great Lakes Research. (Nonlinear mixed effect models, with a negative binomial error structure for error. Makes use of separable functions within ADMB-RE)

Punt, A.E., Smith, A.D.M. and G. Cui. 2002. Evaluation of management tools for Australia's South East Fishery. 2. How well do commonly-used stock assessment methods perform? Mar. Freshwater Res. 53: 631-644.

Punt, A.E., Smith, A.D.M. and G. Cui. 2002. Evaluation of management tools for Australia's South East Fishery. 3. Towards selecting appropriate harvest strategies. Mar. Freshwater. Res. 53: 645-660.

Punt, A.E., Smith, D.C., Thomson, R.B., Haddon, M., He, X. and J.M. Lyle. 2001. Stock assessment of the blue grenadier *Macruronus novaezelandiae* resource off southeastern Australia. Mar. Freshwater Res. 52: 701-717.

Punt, A.E. and M.N. Maunder. 2013. Stock Synthesis: Advancing stock assessment application and research through the use of a general stock assessment computer program. Fisheries Research, 142:1-2.

Roa-Ureta, R. 2010. A Likelihood-Based Model of Fish Growth With Multiple Length Frequency Data. Journal of Agricultural, Biological, and Environmental Statistics, Volume 15, Number 3, Pages 416–429. (ADMB was used to fit a hierarchical marginal likelihood model based on the multivariate normal distribution to estimate parameters of Schnute's growth model with multiple length-frequency data)

Roel, B.A., De Oliveira, J.A.A, and Beggs, S. 2009. A two-stage biomass model for Irish Sea herring allowing for additional variance in the recruitment index caused by mixing of stocks. ICES Journal of Marine Science, 66: 1808-1813.

Rutter, M.A., and J.R. Bence. 2003. An improved method to estimate sea lamprey wounding rate on hosts with application to lake trout in Lake Huron. Journal of Great Lakes Research 29 (Supplement 1): 320-331.

Sadykova, D., Skurdal, J., Sadykov, A., Taugbol, T., and Hessen, D.O. 2009. Modelling crayfish population dynamics using catch data: A size-structured model. Ecological Modelling, 220: 2727-2733.

Schnute, J.T. Richards, L.J. and Olsen, N. 1998. Statistics, software, and fish stock assessment. In fishery stock assessment models, edited by F. Funk, T.J. Quinn II, J. Heifetz, J.N. Ianelli, J.E. Powers, J.F. Schweigert, P.J. Sullivan, and C.I. Zhang, Alaska Sea Grant College Program Report No. AK-SG-98-01, University of Alaska Fairbanks, 1998.

Schweder, T., Sadykova, D., Rugh, D., and Koski, W. 2010. Population estimates from aerial photographic surveys of naturally and variably marked bowhead whales. Journal of Agricultural, Biological, and Environmental Statistics, 15(1): 1–19.

Schweizer, P. E. Jager, H. I. 2011. Modeling Regional Variation in Riverine Fish Biodiversity in the Arkansas–White–Red River Basin, Transactions of the American Fisheries Society, 140:5, 1227-1239. (ADMB was used in modeling of fish richness to fit generalized linear mixed-effect models in R)

Senina I., Sibert J., Lehodey P., 2008. Parameter estimation for basin-scale ecosystem-linked population models of large pelagic predators: application to skipjack tuna. Progress in Oceanography, 78: 319-335.

Sibert, J. R., J. Hampton, D. A. Fournier, and P. J. Bills. 1999. An advection-diffusion-reaction model for the estimation of fish movement parameters from tagging data, with application to skipjack tuna (*Katsuwonus pelamis*). Can. J. Fish. Aquat. Sci. 56: 925-938.

Sitar, S.P., J.R. Bence, J. Johnson, M.P. Ebener, and W.W. Taylor. 1999. Lake trout mortality and abundance in Southern Lake Huron. North American Journal of Fisheries Management 19:881-900

Skaug, H.J. 2002. Automatic differentiation to facilitate maximum likelihood estimation in nonlinear random effects models. Journal of Computational and Graphical Statistics. 11 p. 458-470.

Skaug, H.J. 2006. Markov modulated Poisson processes for clustered line transect data. Environmental and Ecological Statistics, 13: 199-211

Skaug, H.J., Nils Øien, Tore Schweder, Gjermund Bøthun (2004), Abundance of minke whales (*Balaenoptera acutorostrata*) in the Northeastern Atlantic; Canadian Journal of Fisheries and Aquatic Sciences. 61 p. 870-886.

Skaug, H.J., Fournier D. 2006. Automatic Approximation of the Marginal Likelihood in non-Gaussian Hierarchical Models. Computational Statistics and Data Analysis, 51: 699 - 709.

Skaug, H.J., Frimannslund, L., Øien, N. 2007. Historical population assessment of Barents Sea harp sea's (*Pagophilus groenlandicus*). ICES Journal of Marine Science 64**:** 1356-1365.

Smith, A.D.M. and A.E. Punt. 1998. Stock assessment of gemfish (*Rexea solandri*) in eastern Australia using maximum likelihood and Bayesian methods. p. 245-286. In: T.J. Quinn II, F. Funk, J. Heifetz, J.N. Ianelli, J.E. Powers, J.F. Schweigert, P.J. Sullivan and C-I Zhang Ed. Fisheries Stock Assessment Models, Alaska Sea Grant College Program, AK-SG-98-01.

Simon, C. & Booth, A.J. 2007. Population structure and growth of polydorid polychaetes that infest the cultured abalone, *Haliotis midae*. African Journal of Marine Science 29: 499-509. MULTIFAN-type model and ADMB comfortably handles large numbers of parameters.

Sparrevohn, C. R., Nielsen A. and Støttrup J. G. 2002. Diffusion of fish from a single release point. Can. J. Fish. Aquat. Sci. 59(5):844-853

Stewart, I. J. 2007. Defining plausible migration rates based on historical tagging data: a Bayesian mark-recapture model applied to English sole (*Parophrys vetulus*). Fishery Bulletin 105: 470-484. (ADMB was used to construct the population dynamics model. Benefited from the built-in MCMC implementation and the speed of computation)

Stewart, I. J. and K. R. Piner. 2007. Simulation of the estimation of ageing bias inside an integrated assessment of canary rockfish using age estimates from a bomb radiocarbon study. Marine and Freshwater Research 58: 905-913. (ADMB was used to construct the population dynamics model (Stock Synthesis). Benefited from the speed of computation due to the large number of simulations)

Stewart, I. J., A.C. Hicks, I.G. Taylor, J.T. Thorson, C. Wetzel, S. Kupschus. 2013. A comparison of stock assessment uncertainty estimates using maximum likelihood and Bayesian methods implemented with the same model framework. Fisheries Research 142:37-46. (ADMB was used to construct the population dynamics models (Stock Synthesis). Benefited from the built-in MCMC implementation and the speed of computation)

Szalai, E.B., G.W. Fleischer, and J.R. Bence. 2003. Modeling time varying growth using a generalized von Bertalanffy model with application to bloater (*Coregonus hoyi*) growth dynamics in Lake Michigan. Canadian Journal of Fisheries and Aquatic Sciences. 60: 55 66.

Taylor, N., C. J. Walters, and S. J. D. Martell. 2005. A new likelihood for simultaneously estimating von Bertalanffy growth parameters, gear selectivity, and natural and fishing mortality. Can. J. Fish. Aquat. Sci. 62:215-223.

Taylor, N. G., M. K. McAllister, G. L. Lawson, T. Carruthers, and B. A. Block. 2011. Atlantic Bluefin Tuna: A Novel Multistock Spatial Model for Assessing Population Biomass. PLoS ONE 6:e27693. (ADMB was used to the fit age-composition, stock-composition, commercial CPUE, conventional tag and electronic tag data to a multi-stock, spatial model of Atlantic bluefin tuna)

Taylor, I.G., V. Gertseva, R. D. Methot Jr., M. N. Maunder. 2013. A stock–recruitment relationship based on pre-recruit survival, illustrated with application to spiny dogfish shark. Fisheries Research Volume 142:15-21.

Taylor, I.G. and R.D. Methot Jr. 2013. Hiding or dead? A computationally efficient model of selective fisheries mortality. Fisheries Research 142:75-85.

Trenkel, V.M. 2008. A two-stage biomass random effects model for stock assessment without catches: What can be estimated using only biomass survey indices? Canadian Journal of Fisheries and Aquatic Sciences, 65(6): 1024-1035.

Trenkel, V., Skaug, H.J. 2005. Disentangling the effects of trawl efficiency and population abundance on catch data using random effects models. ICES Journal of Marine Science, 62: 1543-1555.

van Poorten, B. T., C. J. Walters, and N. G. Taylor. 2012. A field-based bioenergetics model for estimating time-varying food consumption and growth. Transactions of the American Fisheries Society 141:943-961.

Walsh, C.T., Pease, B.C., Hoyle, S.D., and Booth, D.J. 2006. Variability in growth of longfinned eels among coastal catchments of south-eastern Australia. Journal of Fish Biology 68: 1693-1706.

Wang SP, Sun CL, Punt AE, et al. 2007. Application of the sex-specific age-structured assessment method for swordfish, *Xiphias gladius*, in the North Pacific. Ocean Fisheries Research 84(3): 282-300

Wang SP, Sun CL, Punt AE, et al. 2005. Evaluation of a sex-specific age-structured assessment method for the swordfish, *Xiphias gladius*, in the North Pacific Ocean. Fisheries Research 73(1-2): 79-97.

Wayte, S.E. 2013. Management implications of including a climate-induced recruitment shift in the stock assessment for jackass morwong (*Nemadactylus macropterus*) in south-eastern Australia. Fisheries Research 142:47-55.

Whitten, A.R., N.L. Klaer, G.N. Tuck, R.W. Day, Accounting for cohort-specific variable growth in fisheries stock assessments: A case study from south-eastern Australia. Fisheries Research 142:27-36.

Wilberg, M.J. 2009. Estimation of recreational bag limit noncompliance using contact creel survey data. Fisheries Research. 99: 239-243.

Wilderbuer, T.K. and Turnock, B.J. 2009. Sex-Specific Natural Mortality of Arrowtooth Flounder in Alaska: Implications of a Skewed Sex Ratio on Exploitation and Management. North American Journal of Fisheries Management, 29: 306-322.

Wilson, S.G., Stewart, B.S., Polovina, J. J., Meekan, M. G., Stevens, J.D., Galuardi, B. 2007. Accuracy and precision of archival tag data: a multiple-tagging study conducted on a whale shark (*Rhincodon typus*) in the Indian Ocean Fisheries Oceanography (OnlineEarly Articles). doi:10.1111/j.1365-2419.2007.00450.x

Ye, Y., Dennis, D., and Skewes, T. 2008. Estimating the sustainable lobster (*Panulirus ornatus*) catch in Torres Strait, Australia, using an age-structured stock assessment model. Continental Shelf Research, 28(16): 2160-2167.