References

Dormann, Carsten F., Jane Elith, Sven Bacher, Carsten Buchmann, Gudrun Carl, Gabriel Carré, Jaime R. García Marquéz, et al. 2013. “Collinearity: A Review of Methods to Deal with It and a Simulation Study Evaluating Their Performance.” Ecography 36: 027–46.
Jorgensen, Jeff C., Eric J. Ward, Mark D. Scheuerell, and Richard W. Zabel. 2016. “Assessing Spatial Covariance Among Time Series of Abundance.” Journal Article. Ecology and Evolution 6: 2472–85. https://doi.org/10.1002/ece3.2031.
Lamon, E. C. III, S. R. Carpenter, and C. A. Stow. 1998. “Forecasting PCB Concentrations in Lake Michigan Salmonids: A Dynamic Linear Model Approach.” Ecological Applications 8: 659–68.
Lisi, Peter J., Daniel E. Schindler, Timothy J. Cline, Mark D. Scheuerell, and Patrick B. Walsh. 2015. “Watershed Geomorphology and Snowmelt Control Stream Thermal Sensitivity to Air Temperature.” Journal Article. Geophysical Research Letters 42 (9): 3380–88. https://doi.org/10.1002/2015gl064083.
Ohlberger, J., Mark D. Scheuerell, and Daniel E. Schindler. 2016. Population coherence and environmental impacts across spatial scales: a case study of Chinook salmon.” Journal Article. Ecosphere 7: e01333. https://doi.org/10.1002/ecs2.1333.
Petris, Giovanni, Sonia Petrone, and Patrizia Campagnoli. 2009. Dynamic Linear Models with r. Use r! London: Springer.
Pole, A., M. West, and J. Harrison. 1994. Applied Bayesian Forecasting and Time Series Analysis. New York: Chapman; Hall.
Scheuerell, Mark D., and John G. Williams. 2005. “Forecasting Climate Induced Changes in the Survival of Snake River Spring/Summer Chinook Salmon (Oncorhynchus Tshawytscha).” Fisheries Oceanography 14 (6): 448–57.
Stachura, Megan M., Nathan J. Mantua, and Mark D. Scheuerell. 2014. Oceanographic influences on patterns in North Pacific salmon abundance.” Journal Article. Canadian Journal of Fisheries and Aquatic Sciences 71 (2): 226–35. https://doi.org/10.1139/cjfas-2013-0367.
Zuur, A. F., R. J. Fryer, I. T. Jolliffe, R. Dekker, and J. J. Beukema. 2003. “Estimating Common Trends in Multivariate Time Series Using Dynamic Factor Analysis.” Environmetrics 14 (7): 665–85.