Papers using TMB

List of papers that uses TMB (first come, first served): ###2020

• Gruss A., Gao, J., Thorson, J.T., Roper, C., Thompson, G, Boldt, J., & Lauth, R. (2020). Estimating synchronous changes in condition and density in eastern Bering Sea fishes. Marine Ecology Progress Series. doi: 10.3354/meps13213
• Gao, J., Thorson, J. T., Szuwalski, C., & Wang, H. Y. (2020). Historical dynamics of the demersal fish community in the East and South China Seas. Marine and Freshwater Research. doi: 10.1071/MF18472

2019

• Weiss D.J., Lucas T.C.D., Nguyen M., et al. (2019) Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000–17: a spatial and temporal modelling study. Lancet. doi: 10.1016/S0140-6736(19)31097-9
• Battle K.E., Lucas T.C.D., Nguyen M., et al. (2019) Mapping the global endemicity and clinical burden of Plasmodium vivax, 2000–17: a spatial and temporal modelling study. Lancet. doi: 10.1016/S0140-6736(19)31096-7
• Perreault, A., Zheng, N., & Cadigan, N. (2019). Estimation of growth parameters based on length-stratified age samples. Canadian Journal of Fisheries and Aquatic Sciences. 999:1-12. doi: 10.1139/cjfas-2019-0129
• Dey, R., Cadigan, N.G., & Zheng, N. (2019). Estimation of the Von Bertalanffy growth model when ages are measured with error. Journal of the Statistical Society. doi: 10.1111/rssc.12340

2017

• Whoriskey, K., Auger-Méthé, M., Albertsen, C. M., Whoriskey, F. G., Binder, T. R., Krueger, C. C., and Flemming, J. M. 2017. A Hidden Markov Movement Model for rapidly identifying behavioral states from animal tracks. Ecology and Evolution. doi: 10.1002/ece3.2795
• Thorson, J., Munch, S, and Swain, D. 2017. Estimating partial regulation in spatiotemporal models of community dynamics. Ecology.
• Kai, Mikihiko, Thorson, J. T., Piner, Kevin, and Maunder, Mark N. 2017. Spatio-temporal variation in size-structured populations using fishery data: an application to shortfin mako (Isurus oxyrinchus) in the Pacific Ocean. Canadian Journal of Fisheries and Aquatic Sciences
• Thorson, J.T., Fonner, R., Haltuch, M., Ono, K., and Winker, H. 2017. Accounting for spatiotemporal variation and fisher targeting when estimating abundance from multispecies fishery data. Can. J. Fish. Aquat. Sci. doi:10.1139/cjfas-2015-0598. (http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2015-0598)
• Thorson, J.T., and Barnett, L.A.K. 2017. Comparing estimates of abundance trends and distribution shifts using single- and multispecies models of fishes and biogenic habitat. ICES J. Mar. Sci.
• Wang, S., Cadigan, N.G., and Benoît, H. 2017. Inference about regression parameters using highly stratified survey count data with over-dispersion and repeated measurements. Journal of Applied Statistics. http://www.tandfonline.com/doi/full/10.1080/02664763.2016.1191622
• Auger-Méthé, M., Albertsen, C. M., Jonsen, I. D., Derocher, A. E., Lidgard, D. C., Studholme, K. R., Bowen, W. D., Crossin, G. T., and Flemming, J. M. 2017. Spatiotemporal modelling of marine movement data using Template Model Builder. Marine Ecology Progress Series 565:237-249. doi: 10.3354/meps12019
• Brooks, M. E., Clements, C., Pemberton, J., and Ozgul, A. 2017. Estimation of Individual Growth Trajectories When Repeated Measures Are Missing. The American Naturalist 190(3): 377-388. https://doi.org/10.1086/692797
• Baktoft, H., Gjelland, K. Ø., Økland, F., and Thygesen, U. H. 2017. Positioning of aquatic animals based on time-of-arrival and random walk models using YAPS (Yet Another Positioning Solver). Scientific Reports, 7(1), 14294. doi: 10.1038/s41598-017-14278-z
• Fan Zhang, Rick M. Rideout, Noel G. Cadigan. (2019). Spatiotemporal variations in juvenile mortality and cohort strength of Atlantic cod (Gadus morhua) off Newfoundland and Labrador. Canadian Journal of Fisheries and Aquatic Sciences, 77(3): 625-635. doi:10.1139/cjfas-2019-0156
• Nan Zheng and Noel Cadigan. (2019). Exact Likelihood for Basic Response-Stratified Sampling, With Application to Von Bertalanffy Growth Model Estimation. Open Journal of Statistics, 9: 623-642. doi: 0.4236/ojs.2019.96040
• Van Beveren, E., Duplisea, D., Castonguay, M., Doniol-Valcroze, T., Plourde, S., & Cadigan, N. G. (2017). How catch underreporting can bias stock assessment of and advice for northwest Atlantic mackerel and a possible resolution using censored catch. Fisheries research, 194, 146-154. doi: 10.1016/j.fishres.2017.05.015
• Thorson, J. T., Munch, S. B., Cope, J. M., & Gao, J. (2017). Predicting life history parameters for all fishes worldwide. Ecological Applications, 27(8), 2262-2276. doi: 10.1002/eap.1606
• Cadigan, N. G., Wade, E., & Nielsen, A. (2017). A spatiotemporal model for snow crab (Chionoecetes opilio) stock size in the southern Gulf of St. Lawrence. Canadian Journal of Fisheries and Aquatic Sciences, 74(11), 1808-1820. doi: 10.1139/cjfas-2016-0260

2016

• C. Konrad, N.G. Cadigan, and J. Brattey. 2016. Reporting rate of tagging experiments for cod (Gadus morhua). Environmental and Ecological Statistics. http://link.springer.com/article/10.1007/s10651-016-0344-0?wt_mc=internal.event.1.SEM.ArticleAuthorOnlineFirst
• Albertsen, C. M., Nielsen, A., and Thygesen, U. H. 2016. Choosing the observational likelihood in state-space stock assessment models. Canadian Journal of Fisheries and Aquatic Sciences. doi: 10.1139/cjfas-2015-0532
• Berg, C. W., & Nielsen, A. 2016. Accounting for correlated observations in an age-based state-space stock assessment model. ICES Journal of Marine Science, 73(7), 1788-1797.
• Thorson, J., Pinsky, M., and Ward, E. 2016. Model-based inference for estimating shifts in species distribution, area occupied, and center of gravity. Methods in Ecology and Evolution, 7(8): 990–1002. DOI: 10.1111/2041-210X.12567. (http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12567/abstract?campaign=woletoc)
• Kristensen, K., Nielsen, A., Berg, C.W., Skaug, H.J., Bell, B. 2016, TMB: Automatic Differentiation and Laplace Approximation https://www.jstatsoft.org/article/view/v070i05
• Cadigan, N.G. 2016. A state-space stock assessment model for northern cod, including under-reported catches and variable natural mortality rates." Canadian Journal of Fisheries and Aquatic Sciences 73(2), 296-308 (http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2015-0047)
• Webber, D.N., and Thorson, J.T. 2016. Variation in growth among individuals and over time: A case study and simulation experiment involving tagged Antarctic toothfish. Fish. Res. 180: 67–76. doi:10.1016/j.fishres.2015.08.016. (http://www.sciencedirect.com/science/article/pii/S016578361530062X)
• Thorson, J.T., and Kristensen, K. 2016. Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples. Fish. Res. 175: 66–74. doi:10.1016/j.fishres.2015.11.016. (http://www.sciencedirect.com/science/article/pii/S0165783615301399)
• Auger-Méthé, M., Field, C., Albertsen, C.M., Derocher, A.E., Lewis, M.A., Jonsen, I.D. and Mills Flemming, J. 2016. State-space model's dirty little secrets: even simple linear Gaussian models can have estimation problems. Scientific Reports 6:26677. doi: 10.1038/srep26677. (http://www.nature.com/articles/srep26677)
• Thorson, J.T., Jannot, J.E., and Somers, K. In press. Using spatio-temporal models of population growth and movement to monitor overlap between human impacts and population density. J. Appl. Ecol. (http://onlinelibrary.wiley.com/doi/10.1111/1365-2664.12664/abstract)
• Thorson, J.T., Ianelli, J.N., Larsen, E., Ries, L., Scheuerell, M.D., Szuwalski, C., and Zipkin, E. 2016. Joint dynamic species distribution models: a tool for community ordination and spatiotemporal monitoring. Glob. Ecol. Biogeogr. 25(9): 1144–1158. doi:10.1111/geb.12464. (http://onlinelibrary.wiley.com/doi/10.1111/geb.12464/abstract.)
• Thorson, J.T., Rindorf, A., Gao, J., Hanselman, D.H., and Winker, H. 2016. Density-dependent changes in effective area occupied for sea-bottom-associated marine fishes. Proc R Soc B 283(1840): 20161853. doi:10.1098/rspb.2016.1853. (http://rspb.royalsocietypublishing.org/content/283/1840/20161853)

2015

• Thorson, J.T., Ianelli, J.N., Munch, S.B., Ono, K., and Spencer, P.D. 2015. Spatial delay-difference models for estimating spatiotemporal variation in juvenile production and population abundance. Can. J. Fish. Aquat. Sci. 72(12): 1897–1915. doi:10.1139/cjfas-2014-0543. (http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2014-0543)
• Thorson, J., Skaug, H., Kristensen, K., Shelton, A., Ward, E., Harms, J., and Benante, J. 2015. The importance of spatial models for estimating the strength of density dependence. Ecology. 96:1202–1212. (http://www.esajournals.org/doi/abs/10.1890/14-0739.1)
• Thorson, J. Shelton, A., Ward, E., and Skaug, H. 2015. Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. ICES Journal of Marine Science. 72 (5): 1297-1310. http://icesjms.oxfordjournals.org/content/72/5/1297.abstract?etoc
• Thorson, J.T., Scheuerell, M.D., Shelton, A.O., See, K.E., Skaug, H.J., and Kristensen, K. 2015. Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range. Methods Ecol. Evol. 6(6): 627–637. doi:10.1111/2041-210X.12359. (http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12359/abstract)
• Thorson, J. 2015. Spatio-temporal variation in fish condition is not consistently explained by density, temperature, or season for Northeast Pacific groundfishes. Mar. Ecol. Progress Series. 526:101-112. http://www.int-res.com/abstracts/meps/v526/p101-112/
• Albertsen, C. M., Whoriskey, K., Yurkowski, D., Nielsen, A., and Flemming, J. M. 2015. Fast fitting of non-Gaussian state-space models to animal movement data via Template Model Builder. Ecology 96(10):2598-2604. doi: 10.1890/14-2101.1
• Karlsen, J. D., Krag, L. A., Albertsen, C. M., and Frandsen, R. P. 2015. From Fishing to Fish Processing: Separation of Fish from Crustaceans in the Norway Lobster-Directed Multispecies Trawl Fishery Improves Seafood Quality. PLoS ONE 10(11):e0140864. doi: 10.1371/journal.pone.0140864