Thorson, J.T., 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. 74, 1311–1321. https://doi.org/10.1093/icesjms/fsw193
Thorson, J.T., 2019. Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fish. Res. 210, 143–161. https://doi.org/10.1016/j.fishres.2018.10.013
Correlated spatio-temporal variation among species (a.k.a. “joint species distribution models”)
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. url: http://onlinelibrary.wiley.com/doi/10.1111/geb.12464/abstract.
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. url: http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12359/abstract
Correlated spatio-temporal variation among years (a.k.a. “Empirical Orthogonal functions”)
Thorson, J.T., Ciannelli, L. and Litzow, M. (In press) Defining indices of ecosystem variability using biological samples of fish communities: a generalization of empirical orthogonal functions. Progress In Oceanography.
Index of abundance
Thorson, J.T., Shelton, A.O., Ward, E.J., Skaug, H.J., 2015. Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. ICES J. Mar. Sci. J. Cons. 72(5), 1297–1310. doi:10.1093/icesjms/fsu243. URL: http://icesjms.oxfordjournals.org/content/72/5/1297
Standardizing samples of size/age-composition data
Thorson, J. T., and Haltuch, M. A. 2018. Spatio-temporal analysis of compositional data: increased precision and improved workflow using model-based inputs to stock assessment. Canadian Journal of Fisheries and Aquatic Sciences. doi:10.1139/cjfas-2018-0015. URL: http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2018-0015#.W0oloTpKiUk
Range shift metrics
Thorson, J.T., Pinsky, M.L., Ward, E.J., 2016. Model-based inference for estimating shifts in species distribution, area occupied, and center of gravity. Methods Ecol. Evol. 7(8), 990-1008. doi:10.1111/2041-210X.12567. URL: http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12567/full
Effective area occupied metric
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. URL: http://rspb.royalsocietypublishing.org/content/283/1840/20161853.
Spatio-temporal statistical methods
Thorson, J.T., Skaug, H.J., Kristensen, K., Shelton, A.O., Ward, E.J., Harms, J.H., Benante, J.A., 2014. The importance of spatial models for estimating the strength of density dependence. Ecology 96, 1202–1212. doi:10.1890/14-0739.1. URL: http://www.esajournals.org/doi/abs/10.1890/14-0739.1
Shelton, A.O., Thorson, J.T., Ward, E.J., Feist, B.E., 2014. Spatial semiparametric models improve estimates of species abundance and distribution. Can. J. Fish. Aquat. Sci. 71, 1655–1666. doi:10.1139/cjfas-2013-0508. URL: http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2013-0508#.VMafDf7F_h4
Accounting for fish shoals using robust observation models
Thorson, J. T., I. J. Stewart, and A. E. Punt. 2012. Development and application of an agent-based model to evaluate methods for estimating relative abundance indices for shoaling fish such as Pacific rockfish (Sebastes spp.). ICES Journal of Marine Science 69:635–647. URL: http://icesjms.oxfordjournals.org/content/69/4/635
Thorson, J. T., I. Stewart, and A. Punt. 2011. Accounting for fish shoals in single- and multi-species survey data using mixture distribution models. Canadian Journal of Fisheries and Aquatic Sciences 68:1681–1693. URL: http://www.nrcresearchpress.com/doi/abs/10.1139/f2011-086#.VMafcf7F_h4
Accounting for variation among vessels
Helser, T.E., Punt, A.E., Methot, R.D., 2004. A generalized linear mixed model analysis of a multi-vessel fishery resource survey. Fish. Res. 70, 251–264. doi:10.1016/j.fishres.2004.08.007. url: http://www.sciencedirect.com/science/article/pii/S0165783604001705
Thorson, J.T., Ward, E.J., 2014. Accounting for vessel effects when standardizing catch rates from cooperative surveys. Fish. Res. 155, 168–176. doi:10.1016/j.fishres.2014.02.036. url: http://www.sciencedirect.com/science/article/pii/S0165783614000836
Accounting for fisher targetting in fishery-dependent data
Thorson, J.T., Fonner, R., Haltuch, M., Ono, K., and Winker, H. In press. 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. URL: http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2015-0598
Dolder, P.J., Thorson, J.T., Minto, C., 2018. Spatial separation of catches in highly mixed fisheries. Sci. Rep. 8, 13886. https://doi.org/10.1038/s41598-018-31881-w
Bias-correction of estimated indices of abundance
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. url: http://www.sciencedirect.com/science/article/pii/S0165783615301399
Estimating and attributing variation in size-structured distribution
Kai, M., Thorson, J. T., Piner, K. R., and Maunder, M. 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. doi:10.1139/cjfas-2016-0327. URL: http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2016-0327#.W0olqjpKiUk.
Thorson, J. T., Ianelli, J. N., and Kotwicki, S. 2018. The relative influence of temperature and size-structure on fish distribution shifts: A case-study on Walleye pollock in the Bering Sea. Fish and Fisheries. doi:10.1111/faf.12225. URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/faf.12225.
Estimating fishing impacts using spatial surplus production modelling
Thorson, J. T., Jannot, J., and Somers, K. 2017. Using spatio-temporal models of population growth and movement to monitor overlap between human impacts and fish populations. Journal of Applied Ecology, 54: 577–587.doi:10.1111/1365-2664.12664. URL: https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.12664
Estimating species interactions using multispecies Gompertz model
Thorson, J. T., Munch, S. B., and Swain, D. P. 2017. Estimating partial regulation in spatiotemporal models of community dynamics. Ecology, 98: 1277–1289. doi:10.1002/ecy.1760. URL: https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecy.1760
Thorson, J.T., Adams, G., Holsman, K., In press. Spatio-temporal models of intermediate complexity for ecosystem assessments: A new tool for spatial fisheries management. Fish and Fisheries. https://doi.org/10.1111/faf.12398
Estimating synchrony among species and locations as measure of risk-exposure
Thorson, J.T., Scheuerell, M.D., Olden, J.D., Schindler, D.E., 2018. Spatial heterogeneity contributes more to portfolio effects than species variability in bottom-associated marine fishes. Proc R Soc B 285, 20180915. https://doi.org/10.1098/rspb.2018.0915
Forecasting future changes in distribution or abundance
Thorson, 2019. Forecast skill for predicting distribution shifts: A retrospective experiment for marine fishes in the Eastern Bering Sea. Fish Fish. 20(1): 159-173. https://doi.org/10.1111/faf.12330
Combining multiple types of data (e.g., biomass, count, encounter)
Grüss, A. and Thorson, J.T. (2019) Developing spatio-temporal models using multiple data types for evaluating population trends and habitat usage. ICES Journal of Marine Science 76, 1748–1761. doi:10.1093/icesjms/fsz075.
Spatially varying coefficient models and their use for fisheries oceanography
Thorson, J.T., 2019. Measuring the impact of oceanographic indices on species distribution shifts: The spatially varying effect of cold-pool extent in the eastern Bering Sea. Limnol. Oceanogr. 64, 2632–2645. https://doi.org/10.1002/lno.11238