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references.Rmd
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---
title: "References"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Textbooks & vignettes with specific `R` examples
**The main class reference is the ATSA Lab Book**
HSW18b: Holmes, E. E., M. D. Scheuerell, and E. J. Ward. Applied Time Series Analysis for Fisheries and Environmental data. eBook. [Available here](https://atsa-es.github.io/atsa-labs/)
**In addition, we will use these as references for the class.**
CM09: Cowpertwait PSP, Metcalfe AV. 2009. Introductory Time Series with R. Springer, New York. [Available here](https://link.springer.com/book/10.1007%2F978-0-387-88698-5).
HWS18a: Holmes EE, Ward EJ, Scheuerell MD. 2014. Analysis of Multivariate Time Series Using the MARSS Package. [Available here](https://cran.r-project.org/web/packages/MARSS/vignettes/UserGuide.pdf)
HA18: Hyndman RJ, Athanasopoulos G. 2018. Forecasting: Principles and Practice. eBook. [Available here](https://otexts.org/fpp2/)
H18: Holmes, E. E. Fisheries Catch Forecasting with R. 2018. eBook. [Available here](https://fish-forecast.github.io/Fish-Forecast-Bookdown/)
## Some classic textbooks that you may find helpful
Box GEP, Jenkins GM, Reinsel GC. 2008. Time Series Analysis: Forecasting and Control. John Wiley & Sons, Hoboken, New Jersey.
Brockwell PJ, Davis RA. 2010. Introduction to Time Series and Forecasting. Springer, New York.
Durbin J, Koopman SJ. 2012. Time Series Analysis by State Space Methods. Oxford University Press, Oxford.
Harvey AC. 1991. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge.
Pole A, West M, Harrison J. 1994. Applied Bayesian Forecasting and Time Series Analysis. Chapman & Hall/CRC, Boca Raton, Florida.
Shumway DH, Stoffer DS. 2006. Time Series Analysis and Its Applications: With R Examples. Springer, New York. R scripts and data here
West M, Harrison J. 1997. Bayesian Forecasting and Dynamic Models. Springer, New York.
Petris G, Petrone S, Campaginoli P. 2009. Dynamic Linear Models with R. Springer, New York.
## Papers/vignettes
Andrews, K.S., G.D. Williams, J.F. Samhouri, K.N. Marshall, V. Gertseva, and P.S. Levin. In press. The legacy of a crowded ocean: indicators, status, and trends of anthropogenic pressures in the California Current ecosystem. Environmental Conservation.
Baudron, A.R., C.L. Needle, A.D. Rijnsdorp, and C.T. Marshall. 2014. Warming temperatures and smaller body sizes: synchronous changes in growth of North Sea fishes. Global Change Biology, 20(4):1023-1031.
Britten, G.L., M. Dowd, C. Minto, F. Ferretti, F. Boero, and H.K. Lotze. 2014. Predator decline leads to decreased stability in a coastal fish community. Ecology Letters, 17: 1518–1525.
Goertler, P.A.L., M.D. Scheuerell, C.A. Simenstad, and D.L. Bottom. 2016. Estimating common growth patterns in juvenile Chinook salmon (Oncorhynchus tshawytscha) from diverse genetic stocks and a large spatial extent. PLoS ONE 11:e0162121
Hampton, S.E., E.E. Holmes, L.P. Scheef, M.D. Scheuerell, S.L. Katz, D.E. Pendleton, and E.J. Ward 2013. Quantifying effects of abiotic and biotic drivers on community dynamics with multivariate autoregressive (MAR) models. Ecology 94:2663–2669.
Harrison, Philip J., Ilkka Hanski, and Otso Ovaskainen. 2011. Bayesian state-space modeling of metapopulation dynamics in the Glanville fritillary butterfly. Ecological Monographs 81:581–598.
Holmes EE, Ward EJ, Wills K. 2012. MARSS: multivariate autoregressive state-space models for analyzing time-series data. The R Journal. 4(1): 11-19
Hyndman RJ, Khandakar Y. 2008. Automatic time series forecasting: the forecast package for R. Journal of Statistical Software 27(3): 1-22
Ives AR, Dennis B, Cottingham, KL, Carpenter SR. 2003. Estimating community stability and ecological interactions from time series data. Ecological Monographs 73:301–330.
Maurer, B.A., J.R. Bence, and T.O. Brenden. 2014. Assessing Dynamics of Lake Huron Fish Communities using Dynamic Factor
Analysis. QFC Technical Report T2014-01 prepared for Ontario Ministry of Natural Resources.
Ohlberger, J., M.D. Scheuerell, and D.E. Schindler. 2016. Population coherence and environmental impacts across spatial scales: a case study of Chinook salmon. Ecosphere 7:e01333
Rigot, T., A. Conte, M. Goffredo, E. Ducheyne, G. Hendrickx, and M. Gilbert. 2012. Predicting the spatio-temporal distribution of
Culicoides imicola in Sardinia using a discrete-time population model. Parasites & Vectors 2012, 5:270.
Sandlund, O.T., K.Ø. Gjelland, T. Bøhn, R. Knudsen, P.-A. Amundsen. 2013. Contrasting Population and Life History Responses of a Young Morph-Pair of European Whitefish to the Invasion of a Specialised Coregonid Competitor, Vendace. PLoS ONE 8(7): e68156. doi: 10.1371/journal.pone.0068156.
Scheuerell MD, Williams JG. 2005. Forecasting climate-induced changes in the survival of Snake River spring/summer Chinook salmon (Oncorhynchus tshawytscha). Fisheries Oceanography 14: 448-457
See, K.E. and E.E. Holmes. 2015. Reducing bias and improving precision in species extinction forecasts. Ecological Applications 25: 1157-1165.
Simonis, J.L. 2013. Predator ontogeny determines trophic cascade strength in freshwater rock pools. Ecosphere 4:art62.
Sinclair, A.R.E., K.L. Metzger, J.M. Fryxell, C. Packer, A.E. Byrom, M.E. Craft, K. Hampson, T. Lembo, S. M. Durant, G.J. Forrester, J. Bukombe, J. Mchetto, J. Dempewolf, R. Hilborn, S. Cleaveland, A. Nkwabi, A. Mosser, and S.A.R. Mduma 2013. Asynchronous food-web pathways could buffer the response of Serengeti predators to El Niño Southern Oscillation. Ecology 94:1123–1130.
Stachura, M.M., N. J. Mantua, and M. D. Scheuerell. 2014. Oceanographic influences on patterns in North Pacific salmon abundance. Canadian Journal of Fisheries and Aquatic Sciences, 71(2): 226-235. doi: 10.1139/cjfas-2013-0367
Ward, E.J., H. Chirrakal, M. González-Suárez, D. Aurioles-Gamboa, E.E. Holmes, L. Gerber. 2010. Applying Multivariate-state-space Models to Detect Spatial clustering of California sea lions in the Gulf of California, Mexico. Journal of Applied Ecology, 47:47-56.
Zuur AF, Tuck ID, Bailey N. 2003. Dynamic factor analysis to estimate common trends in fisheries time series. Can J Fish Aquat Sci 60: 542-552.
Zuur, AF, Fryer RJ, Jolliffe IT, Beukema JJ. 2003. Estimating common trends in multivariate time series using dynamic factor analysis. Environmetrics 14: 665-685.