From 6935749e3c4f5f977225789dd2a81bfedd54e981 Mon Sep 17 00:00:00 2001 From: Mark Scheuerell Date: Mon, 21 Nov 2022 11:01:27 -0800 Subject: [PATCH] rebuilt course website --- docs/License.html | 33 +- docs/asyllabus.html | 46 +-- docs/comp_labs.html | 97 ++---- docs/final_proj.html | 116 ++----- docs/homework.html | 22 +- docs/index.html | 282 +++--------------- docs/lectures.html | 271 +++-------------- docs/list_of_labs.html | 2 +- docs/list_of_lectures.html | 2 +- docs/references.html | 152 +++------- docs/review_guide.html | 36 +-- .../header-attrs.js | 0 .../navigation-1.1/codefolding-lua.css | 9 - docs/site_libs/navigation-1.1/codefolding.js | 2 - docs/student_pubs.html | 122 +++----- syllabus.RData | Bin 5531 -> 5529 bytes 16 files changed, 260 insertions(+), 932 deletions(-) rename docs/site_libs/{header-attrs-2.14 => header-attrs-2.13}/header-attrs.js (100%) delete mode 100644 docs/site_libs/navigation-1.1/codefolding-lua.css diff --git a/docs/License.html b/docs/License.html index ae0eb826..b1a4bcbb 100644 --- a/docs/License.html +++ b/docs/License.html @@ -13,7 +13,7 @@ License - + @@ -307,37 +307,20 @@

License

-

This code was developed by United States federal government employees -as part of their official duties. As such, it is not subject to -copyright protection and is considered “public domain” (see 17 USC § -105). Public domain software can be used by anyone for any purpose, and -cannot be released under a copyright license.

-

Additionally, we waive copyright and related rights in the work -worldwide through the CC0 1.0 Universal public domain dedication.

+

This code was developed by United States federal government employees as part of their official duties. As such, it is not subject to copyright protection and is considered “public domain” (see 17 USC § 105). Public domain software can be used by anyone for any purpose, and cannot be released under a copyright license.

+

Additionally, we waive copyright and related rights in the work worldwide through the CC0 1.0 Universal public domain dedication.

CC0 1.0 Universal Summary

-

This is a human-readable summary of the Legal -Code (read the full text).

+

This is a human-readable summary of the Legal Code (read the full text).

Other Information

-

In no way are the patent or trademark rights of any person affected -by CC0, nor are the rights that other persons may have in the work or in -how the work is used, such as publicity or privacy rights.

-

Unless expressly stated otherwise, the person who associated a work -with this deed makes no warranties about the work, and disclaims -liability for all uses of the work, to the fullest extent permitted by -applicable law. When using or citing the work, you should not imply -endorsement by the author or the affirmer.

+

In no way are the patent or trademark rights of any person affected by CC0, nor are the rights that other persons may have in the work or in how the work is used, such as publicity or privacy rights.

+

Unless expressly stated otherwise, the person who associated a work with this deed makes no warranties about the work, and disclaims liability for all uses of the work, to the fullest extent permitted by applicable law. When using or citing the work, you should not imply endorsement by the author or the affirmer.

diff --git a/docs/asyllabus.html b/docs/asyllabus.html index e6b71e82..ac054288 100644 --- a/docs/asyllabus.html +++ b/docs/asyllabus.html @@ -13,7 +13,7 @@ 2021 Schedule - + @@ -337,8 +337,7 @@

2021 Schedule

5 Jan -Course overview
Properties of time series
Data transformations -
Time series decomposition
+Course overview
Properties of time series
Data transformations
Time series decomposition
Matrices & matrix algebra
(review on your own)
@@ -352,13 +351,10 @@

2021 Schedule

7 Jan -Covariance & correlation
Autocorrelation & Partial -autocorrelation
Cross correlation
White noise
Random walks -
Differencing
+Covariance & correlation
Autocorrelation & Partial autocorrelation
Cross correlation
White noise
Random walks
Differencing
-Writing models in matrix form (through section 2.5)
Basic time -series functions
Autocorrelation
+Writing models in matrix form (through section 2.5)
Basic time series functions
Autocorrelation
linear regression in matrix form
Optional
@@ -369,9 +365,7 @@

2021 Schedule

12 Jan -Autoregressive (AR) models
Moving average (MA) models
Stationary -AR models
Invertible MA models
Using ACF & PACF for model ID -
+Autoregressive (AR) models
Moving average (MA) models
Stationary AR models
Invertible MA models
Using ACF & PACF for model ID

@@ -385,16 +379,13 @@

2021 Schedule

14 Jan -Box-Jenkins method
Fitting ARIMA models with R
Forecasting with -ARIMA models
Seasonal ARIMA models
+Box-Jenkins method
Fitting ARIMA models with R
Forecasting with ARIMA models
Seasonal ARIMA models
-Simulating ARMA models
Fitting ARIMA models
Forecasting with -ARIMA models
Box-Jenkins Methods
+Simulating ARMA models
Fitting ARIMA models
Forecasting with ARIMA models
Box-Jenkins Methods
-ARIMA models
Due next Thurs 11:59 PM PST; email to instructor(s) for -lab
+ARIMA models
Due next Thurs 11:59 PM PST; email to instructor(s) for lab
@@ -444,15 +435,13 @@

2021 Schedule

28 Jan -Regression with autocorrelated errors
Dynamic factor analysis (DFA) -
+Regression with autocorrelated errors
Dynamic factor analysis (DFA)
Fitting DFA models
-Dynamic Factor Analysis
Due next Thurs 11:59 PM PST; email to Mark -
+Dynamic Factor Analysis
Due next Thurs 11:59 PM PST; email to Mark
@@ -480,8 +469,7 @@

2021 Schedule

Fitting DLMs
-Dynamic Linear Models
Due next Thurs 11:59 PM PST; email to -instructor(s) for lab
+Dynamic Linear Models
Due next Thurs 11:59 PM PST; email to instructor(s) for lab
@@ -503,8 +491,7 @@

2021 Schedule

11 Feb -MAP estimation
Multivariate models
DFA
Writing Bayesian -models in Stan
+MAP estimation
Multivariate models
DFA
Writing Bayesian models in Stan
Bayesian estimation
STAN
@@ -518,8 +505,7 @@

2021 Schedule

16 Feb -Multi-model inference and selection
Information criteria -
Cross-validation & LOOIC
+Multi-model inference and selection
Information criteria
Cross-validation & LOOIC

@@ -533,8 +519,7 @@

2021 Schedule

18 Feb -AR(p) Models
Estimating interaction strengths
Gompertz models -
Stability metrics
+AR(p) Models
Estimating interaction strengths
Gompertz models
Stability metrics
Interactions
Stability
B matrix
@@ -562,8 +547,7 @@

2021 Schedule

25 Feb -Frequency domain
Fourier transforms
Spectral analysis -
Wavelet analysis
+Frequency domain
Fourier transforms
Spectral analysis
Wavelet analysis
Fitting EDM models
diff --git a/docs/comp_labs.html b/docs/comp_labs.html index 3676fcdd..add7b367 100644 --- a/docs/comp_labs.html +++ b/docs/comp_labs.html @@ -13,7 +13,7 @@ Computer Labs - + @@ -317,11 +317,9 @@

Meeting days & times

Lab book

-

You can find the electronic lab book here.

+

You can find the electronic lab book here.

-
+
@@ -365,25 +363,18 @@

Lab book

Matrices & matrix algebra
(review on your own)
@@ -394,22 +385,15 @@

Lab book

Eli/Mark
@@ -512,24 +486,12 @@

Lab book

Fitting univariate and mulitvariate state-space models
@@ -730,21 +687,13 @@

Lab book

@@ -782,8 +731,7 @@

Lab book

Fitting EDM models
@@ -837,10 +785,7 @@

Lab book

-html -1
PDF -1
+html 1
PDF 1
 -Optional (not part of grade): Listed at end of -Chapter -1 in the ATSA lab book.
+Optional (not part of grade): Listed at end of Chapter 1 in the ATSA lab book.
Optional
-Key +Key
-Writing models in matrix form (through section 2.5)
Basic time -series functions
Autocorrelation
+Writing models in matrix form (through section 2.5)
Basic time series functions
Autocorrelation
-html -1 html -2

+html 1 html 2

 -Optional (but important to understand course material): End of -Chapter -2 in the ATSA lab book.
+Optional (but important to understand course material): End of Chapter 2 in the ATSA lab book.
Optional
@@ -450,31 +434,21 @@

Lab book

Mark & Eli
-Simulating ARMA models
Fitting ARIMA models
Forecasting with -ARIMA models
Box-Jenkins Methods
+Simulating ARMA models
Fitting ARIMA models
Forecasting with ARIMA models
Box-Jenkins Methods
-html -1 html -2

+html 1 html 2

 -Intro to ts: problems at end of -Chapter -4 in the ATSA lab book.
Fitting ARMA models: problems at end of -Chapter -5 in the ATSA lab book.
+Intro to ts: problems at end of Chapter 4 in the ATSA lab book.
Fitting ARMA models: problems at end of Chapter 5 in the ATSA lab book.
Due next Thurs 11:59 PM PST; email to instructor(s) for lab
-Key 1 Key 2 +Key 1 Key 2
-html -1 html -2

+html 1 html 2

 -HW #2 questions and data: Rmd, -html, -data, -Tips -
+HW #2 questions and data: Rmd, html, data, Tips
Due Sat Jan 30 11:59 PM PST; email to Eli
@@ -622,8 +584,7 @@

Lab book

Fitting DLMs
-html 1

Rmd 1 +html 1

Rmd 1 		Video @@ -673,11 +634,7 @@

Lab book

Bayesian estimation
STAN
-html -1

Rmd -1 +html 1

Rmd 1 		-HW #6: HMM/Bayesian State space models. Rmd, -html, -Rmd -Part 2 JAGS, Part 2 -Online
+HW #6: HMM/Bayesian State space models. Rmd, html, Rmd Part 2 JAGS, Part 2 Online
Due Monday Mar 1 11:59 PM PST; email to Eric & Eli
-Key 1 Key 2 +Key 1 Key 2
-html 1

Rmd 1 +html 1

Rmd 1 		-No homework; Datasets for in class lab Data1, Data2, Data3
+No homework; Datasets for in class lab Data1, Data2, Data3
work on projects
diff --git a/docs/final_proj.html b/docs/final_proj.html index 63c67dec..974cf852 100644 --- a/docs/final_proj.html +++ b/docs/final_proj.html @@ -13,7 +13,7 @@ Final Project - + @@ -384,10 +384,7 @@

Final Project

-

As part of the class, each student will have to write a complete, -publishable (<20 page) paper using the time series analysis -techniques learned in class. See below for details on the structure of -the paper.

+

As part of the class, each student will have to write a complete, publishable (<20 page) paper using the time series analysis techniques learned in class. See below for details on the structure of the paper.

Due Dates

    @@ -400,40 +397,18 @@

    Due Dates

Data sets

-

Students are encouraged to use their own data and the paper may form -a chapter for their thesis/dissertation. However some students might not -have their own data. Students may also use data from the instructors, -public datasets or datasets included in R -libraries.

-

Students without data or a particular project in mind might consider -taking part in the EFI -RCN NEON Ecological Forecast Challenge happening concurrently with -the 2021 Fish 507 course. You do not need to formally participate in the -challenge (i.e. register as a team but you are welcome to do so). All -the data are provided and the challenge lays out the goals (what to -forecast) for each challenge. The Aquatic Ecosystems, Tick Abundance, -and Beetle Abundance challenges would be appropriate for the class.

+

Students are encouraged to use their own data and the paper may form a chapter for their thesis/dissertation. However some students might not have their own data. Students may also use data from the instructors, public datasets or datasets included in R libraries.

+

Students without data or a particular project in mind might consider taking part in the EFI RCN NEON Ecological Forecast Challenge happening concurrently with the 2021 Fish 507 course. You do not need to formally participate in the challenge (i.e. register as a team but you are welcome to do so). All the data are provided and the challenge lays out the goals (what to forecast) for each challenge. The Aquatic Ecosystems, Tick Abundance, and Beetle Abundance challenges would be appropriate for the class.

Other sources of public fisheries data sets are:

@@ -477,69 +448,36 @@

Preparation of final papers

due Fri Mar 12 11:59pm PST

Length

-

Final papers should be no more than 20 pages total, including all -figures, tables and references. Please submit the PDF or Word version of -your paper via email.

+

Final papers should be no more than 20 pages total, including all figures, tables and references. Please submit the PDF or Word version of your paper via email.

Components

Each paper needs to have the following:

-

Title page: include the title, your name, and a -“tweetable abstract” summarizing everything you’ve done in 140 -characters or less. These won’t be launched into the twitterverse, but -as a concise (and exciting!) summary.

-

Abstract page: Summarize briefly (ideally < 250 -words) the novelty of your analysis, key results, and implications for -future work

-

Body / main text: Please include an Introduction / -Methods / Results / Discussion section. You’re free to use any of the -equations from the MARSS manual, or class material to provide equations, -variables, descriptions, etc.

-

References: Please use some kind of bibliography -manager (like Endnote) to format all references consistently.

-

Figures / Tables: Include figures and tables -formatted for the journal of your choice. Examples you might want to -include are: plots of your raw data, plots of underlying state -estimates, plots of future projections, tables of parameter estimates, -model selection (AICc) etc.

+

Title page: include the title, your name, and a “tweetable abstract” summarizing everything you’ve done in 140 characters or less. These won’t be launched into the twitterverse, but as a concise (and exciting!) summary.

+

Abstract page: Summarize briefly (ideally < 250 words) the novelty of your analysis, key results, and implications for future work

+

Body / main text: Please include an Introduction / Methods / Results / Discussion section. You’re free to use any of the equations from the MARSS manual, or class material to provide equations, variables, descriptions, etc.

+

References: Please use some kind of bibliography manager (like Endnote) to format all references consistently.

+

Figures / Tables: Include figures and tables formatted for the journal of your choice. Examples you might want to include are: plots of your raw data, plots of underlying state estimates, plots of future projections, tables of parameter estimates, model selection (AICc) etc.

Style

-

As long as you include page numbers and line numbers, you are free to -use the general formatting guidelines for whichever journal you plan to -eventually submit your paper to. For some examples, see

-

CJFAS: http://www.nrcresearchpress.com/

-

Ecology: http://esapubs.org/esapubs/preparation.htm

-

Journal of Applied Ecology: http://www.journalofappliedecology.org/view/0/authorGuideline.html

+

As long as you include page numbers and line numbers, you are free to use the general formatting guidelines for whichever journal you plan to eventually submit your paper to. For some examples, see

+

CJFAS: http://www.nrcresearchpress.com/

+

Ecology: http://esapubs.org/esapubs/preparation.htm

+

Journal of Applied Ecology: http://www.journalofappliedecology.org/view/0/authorGuideline.html

Peer reviews

Advice on reviewing scientific papers

-

If you are looking for some guidance on writing reviews of scientific -papers, here are some links to various columns, blogs, etc, about -reviewing scientific papers:

-

Arthropod -Ecology Violent -metaphors (don’t let the name scare you) Duke -Writing Lab Examples -of good reviews from Peerage

+

If you are looking for some guidance on writing reviews of scientific papers, here are some links to various columns, blogs, etc, about reviewing scientific papers:

+

Arthropod Ecology Violent metaphors (don’t let the name scare you) Duke Writing Lab Examples of good reviews from Peerage

Guidelines

-

Final papers will be peer-reviewed and reviewed by instructors based -on the following criteria.

-

Example -of a manuscript review for Fish 507.docx

+

Final papers will be peer-reviewed and reviewed by instructors based on the following criteria.

+

Example of a manuscript review for Fish 507.docx

Review Template.docx

Review Template.Rmd

diff --git a/docs/homework.html b/docs/homework.html index 72cd30b4..84a0a059 100644 --- a/docs/homework.html +++ b/docs/homework.html @@ -13,7 +13,7 @@ Homework - + @@ -308,27 +308,15 @@

Homework

-

The homework for each week is listed with the Computer Labs.

+

The homework for each week is listed with the Computer Labs.

Homework format

-

Please submit your homework as an Rmarkdown document (.Rmd), which -will allow you to combine text, equations, and R code into a pdf or html -file. The easiest way to do so is to use the built-in capabilities of -RStudio. For those unfamiliar with Rmarkdown, there is a nice -introduction here on the -first QERM 514 lab. There is also help available in RStudio. Please use -the template below when submitting your homework. Email your file(s) to -the instructor indicated for each week.

+

Please submit your homework as an Rmarkdown document (.Rmd), which will allow you to combine text, equations, and R code into a pdf or html file. The easiest way to do so is to use the built-in capabilities of RStudio. For those unfamiliar with Rmarkdown, there is a nice introduction here on the first QERM 514 lab. There is also help available in RStudio. Please use the template below when submitting your homework. Email your file(s) to the instructor indicated for each week.

diff --git a/docs/index.html b/docs/index.html index bd9c0dd4..20b7157d 100644 --- a/docs/index.html +++ b/docs/index.html @@ -13,7 +13,7 @@ Applied Time Series Analysis - + @@ -380,8 +380,7 @@

Applied Time Series Analysis

-

FISH 550     University of Washington     Spring -Qtr odd years

+

FISH 550     University of Washington     Spring Qtr odd years

@@ -389,25 +388,12 @@

FISH 550     University of Washington     Spring


Course overview

-

This course is intended to give students an overview of the theory -and practical aspects of fitting time series models to fisheries and -environmental data. The course will cover topics ranging from -autocorrelation and crosscorrelation, autoregressive (AR) and moving -average (MA) models, univariate and multivariate state-space models, and -estimating model parameters. This course also covers various aspects of -assessing model performance and evaluating model diagnostics. The course -is focused almost exclusively on problems and analyses in the time -domain, and only briefly addresses methods for the frequency domain. In -general, students will focus on conceptualizing analyses, implementing -analyses, and making inference from the results.

+

This course is intended to give students an overview of the theory and practical aspects of fitting time series models to fisheries and environmental data. The course will cover topics ranging from autocorrelation and crosscorrelation, autoregressive (AR) and moving average (MA) models, univariate and multivariate state-space models, and estimating model parameters. This course also covers various aspects of assessing model performance and evaluating model diagnostics. The course is focused almost exclusively on problems and analyses in the time domain, and only briefly addresses methods for the frequency domain. In general, students will focus on conceptualizing analyses, implementing analyses, and making inference from the results.


Textbook

-

Holmes, E. E., M. D. Scheuerell, and E. J. Ward. Applied Time Series -Analysis for Fisheries and Environmental data. eBook. Available -here

+

Holmes, E. E., M. D. Scheuerell, and E. J. Ward. Applied Time Series Analysis for Fisheries and Environmental data. eBook. Available here


@@ -415,37 +401,26 @@

Learning objectives

By the end of the quarter, students should be able to:

  • Understand the elements to classical decomposition

    • -

  • Understand how to use ACF and PACF to identify orders of -ARMA(p,q) models for time series data

    • -

  • Apply appropriate diagnostic measures to identify any -shortcomings in model assumptions

    • -

  • Understand how to use information theoretic methods and cross -validation for model selection

    • -

  • Understand how to combine state and observation models into a -state-space model

    • -

  • Use multivariate time series models with covariates to identify -influential explanatory variables and do perturbation analyses

    • -

  • Use Dynamic Factor Analysis to identify common patterns among -many time series

    • -

  • Use Dynamic Linear Models to allow for changing relationships -between a response variable and any explanatory variable(s)

    • -

  • Prepare forecasts with uncertainty using time series -models

    • +

  • Understand how to use ACF and PACF to identify orders of ARMA(p,q) models for time series data

    • +

  • Apply appropriate diagnostic measures to identify any shortcomings in model assumptions

    • +

  • Understand how to use information theoretic methods and cross validation for model selection

    • +

  • Understand how to combine state and observation models into a state-space model

    • +

  • Use multivariate time series models with covariates to identify influential explanatory variables and do perturbation analyses

    • +

  • Use Dynamic Factor Analysis to identify common patterns among many time series

    • +

  • Use Dynamic Linear Models to allow for changing relationships between a response variable and any explanatory variable(s)

    • +

  • Prepare forecasts with uncertainty using time series models


Instructors

-

Eli -Holmes
+

Eli Holmes
Research Fish Biologist, NOAA Fisheries
eeholmes@uw.edu

-

Eric -Ward
+

Eric Ward
Statistician, NOAA Fisheries
warde@uw.edu

-

Mark -Scheuerell
+

Mark Scheuerell
Associate Professor, School of Aquatic & Fishery Sciences
scheuerl@uw.edu


@@ -468,28 +443,15 @@

Office hours

Pre-requisites

-

Students should have a working knowledge of the R computing -software, such as that provided in FISH 552/553. Students should also -have an understanding of basic probability and statistical -inference.

+

Students should have a working knowledge of the R computing software, such as that provided in FISH 552/553. Students should also have an understanding of basic probability and statistical inference.


Classroom conduct

-

We are dedicated to providing a welcoming and supportive learning -environment for all students, regardless of their background, identity, -physical appearance, or manner of communication. Any form of language or -behavior used to exclude, intimidate, or cause discomfort will not be -tolerated. This applies to all course participants (instructor, -students, guests). In order to foster a positive and professional -learning environment, we ask the following:

+

We are dedicated to providing a welcoming and supportive learning environment for all students, regardless of their background, identity, physical appearance, or manner of communication. Any form of language or behavior used to exclude, intimidate, or cause discomfort will not be tolerated. This applies to all course participants (instructor, students, guests). In order to foster a positive and professional learning environment, we ask the following:

    -

  • Please let us know if you have a name or set of preferred -pronouns that you would like us to use

    • -

  • Please let us know if anyone in class says something -that makes you feel uncomfortable[1]

    • +

  • Please let us know if you have a name or set of preferred pronouns that you would like us to use

    • +

  • Please let us know if anyone in class says something that makes you feel uncomfortable[1]

In addition, we encourage the following kinds of behaviors:

    @@ -498,243 +460,97 @@

    Classroom conduct

  • Acknowledge different viewpoints and experiences

  • Gracefully accept constructive criticism

-

Although we strive to create and use inclusive materials in this -course, there may be overt or covert biases in the course material due -to the lens with which it was written. Your suggestions about how to -improve the value of diversity in this course are encouraged and -appreciated.

-

Please note: If you believe you have been a victim -of an alleged violation of the Student -Conduct Code or you are aware of an alleged violation of the Student -Conduct Code, you have the right to report it to the -University.

+

Although we strive to create and use inclusive materials in this course, there may be overt or covert biases in the course material due to the lens with which it was written. Your suggestions about how to improve the value of diversity in this course are encouraged and appreciated.

+

Please note: If you believe you have been a victim of an alleged violation of the Student Conduct Code or you are aware of an alleged violation of the Student Conduct Code, you have the right to report it to the University.


Access & accommodations

-

All students deserve access to the full range of learning -experiences, and the University of Washington is committed to creating -inclusive and accessible learning environments consistent with federal -and state laws. If you feel like your performance in class is being -impacted by your experiences outside of class, please talk with us.

+

All students deserve access to the full range of learning experiences, and the University of Washington is committed to creating inclusive and accessible learning environments consistent with federal and state laws. If you feel like your performance in class is being impacted by your experiences outside of class, please talk with us.

Disabilities

-

If you have already established accommodations with Disability -Resources for Students (DRS), please communicate your approved -accommodations to us at your earliest convenience so we can discuss your -needs in this course. If you have not yet established services through -DRS, but have a temporary health condition or permanent disability that -requires accommodations (e.g., mental health, learning, vision, -hearing, physical impacts), you are welcome to contact DRS at -206-543-8924 or via email or their website. DRS offers -resources and coordinates reasonable accommodations for students with -disabilities and/or temporary health conditions. Reasonable -accommodations are established through an interactive process between -you, your instructor(s) and DRS.

+

If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to us at your earliest convenience so we can discuss your needs in this course. If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (e.g., mental health, learning, vision, hearing, physical impacts), you are welcome to contact DRS at 206-543-8924 or via email or their website. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS.

Religious observances

-

Students who expect to miss class or assignments as a consequence of -their religious observance will be provided with a reasonable -accommodation to fulfill their academic responsibilities. Absence from -class for religious reasons does not relieve students from -responsibility for the course work required during the period of -absence. It is the responsibility of the student to provide the -instructor with advance notice of the dates of religious holidays on -which they will be absent. Students who are absent will be offered an -opportunity to make up the work, without penalty, within a reasonable -time, as long as the student has made prior arrangements.

+

Students who expect to miss class or assignments as a consequence of their religious observance will be provided with a reasonable accommodation to fulfill their academic responsibilities. Absence from class for religious reasons does not relieve students from responsibility for the course work required during the period of absence. It is the responsibility of the student to provide the instructor with advance notice of the dates of religious holidays on which they will be absent. Students who are absent will be offered an opportunity to make up the work, without penalty, within a reasonable time, as long as the student has made prior arrangements.


Technology

-

This course will revolve around hands-on computing exercises that -demonstrate the topics of interest. Therefore, students are strongly -recommended to bring their own laptop to class, although students are -certainly free to work with one another. For students without -access to a personal laptop: it is possible to check out UW -laptops for an entire quarter (see the Student -Services office for details).

-

All of the software we will be using is free and platform -independent, meaning students may use macOS, Linux, or Windows operating -systems. In addition to a web browser, we will be using the free R software and -the desktop version of the R -Studio integrated development environment (IDE). We will -also be using various packages not contained in the base installation of -R, but we will wait and install them at the necessary -time. The instructor will be available during the first week of class to -help students troubleshoot any software installation problems.

-

Students will also be required to have a user account on GitHub, which we will be -using for file hosting and communications via “issues”. If you do not -already have an account, you can sign up for a free one here. The -instructor will provide training on how to use the intended features in -GitHub.

+

This course will revolve around hands-on computing exercises that demonstrate the topics of interest. Therefore, students are strongly recommended to bring their own laptop to class, although students are certainly free to work with one another. For students without access to a personal laptop: it is possible to check out UW laptops for an entire quarter (see the Student Services office for details).

+

All of the software we will be using is free and platform independent, meaning students may use macOS, Linux, or Windows operating systems. In addition to a web browser, we will be using the free R software and the desktop version of the R Studio integrated development environment (IDE). We will also be using various packages not contained in the base installation of R, but we will wait and install them at the necessary time. The instructor will be available during the first week of class to help students troubleshoot any software installation problems.

+

Students will also be required to have a user account on GitHub, which we will be using for file hosting and communications via “issues”. If you do not already have an account, you can sign up for a free one here. The instructor will provide training on how to use the intended features in GitHub.

Teaching methodology

-

This course will introduce new material primarily through prepared -slides and hands-on demonstrations. Students will be expected to work -both individually and collaboratively (to the extent possible given the -current conditions); course content and evaluation will emphasize the -communication of ideas and the ability to think critically more so than -a specific pathway or method. Other areas of this website provide an -overview of the topics to be covered, including links to weekly reading -assignments, lecture materials, computer labs, and homework -assignments.

+

This course will introduce new material primarily through prepared slides and hands-on demonstrations. Students will be expected to work both individually and collaboratively (to the extent possible given the current conditions); course content and evaluation will emphasize the communication of ideas and the ability to think critically more so than a specific pathway or method. Other areas of this website provide an overview of the topics to be covered, including links to weekly reading assignments, lecture materials, computer labs, and homework assignments.


Communication

-

This course will involve a lot of communication between and -among students and the instructor. Short questions should be addressed -to us via email; we will try my best to respond to your message within -24 hours. Under more normal circumstances, detailed questions would be -addressed to us in person–either after class or during a scheduled -meeting. In this case, however, we will schedule one-on-one or group -video calls as needed.

-

In addition to email and video, we will use the “Issues” feature in -GitHub to ask questions and assist others. -Specifically, questions and answers can be posted to the issues in the -course repository here.

+

This course will involve a lot of communication between and among students and the instructor. Short questions should be addressed to us via email; we will try my best to respond to your message within 24 hours. Under more normal circumstances, detailed questions would be addressed to us in person–either after class or during a scheduled meeting. In this case, however, we will schedule one-on-one or group video calls as needed.

+

In addition to email and video, we will use the “Issues” feature in GitHub to ask questions and assist others. Specifically, questions and answers can be posted to the issues in the course repository here.


Evaluation

-

Students will be evaluated on their knowledge of course content and -their ability to communicate their understanding of the material via -individual homework assignments (30%), a final project (40%), peer -reviews (20%), and class participation (10%). There will be 6 homework -assignments, each of which will count toward 5% of the final grade. -Please note, all assignments must be turned in to achieve a -passing grade.

+

Students will be evaluated on their knowledge of course content and their ability to communicate their understanding of the material via individual homework assignments (30%), a final project (40%), peer reviews (20%), and class participation (10%). There will be 6 homework assignments, each of which will count toward 5% of the final grade. Please note, all assignments must be turned in to achieve a passing grade.

Homework (30%)

-

Homework will be assigned each Thursday and is due by 11:59 PM PST on -the following Thursday. It will consist of some short answers and -R code based on topics covered in lab. There will be 6 -assignments worth 5% each. Your learning in the class will be greatly -enhanced by doing the homework which consist of applying the material -you learn in each lecture to a data set.

+

Homework will be assigned each Thursday and is due by 11:59 PM PST on the following Thursday. It will consist of some short answers and R code based on topics covered in lab. There will be 6 assignments worth 5% each. Your learning in the class will be greatly enhanced by doing the homework which consist of applying the material you learn in each lecture to a data set.

Individual project (40%)

-

Each student will have to write a complete, publishable (<20 page) -paper that may, or may not, serve as a component of their -thesis/dissertation. Given that some students might not have their own -data, students may also use data from the instructors, public datasets -or datasets included in R libraries. See list of -prior student projects for the types of projects done is prior -years.

-

The techniques you will be learning would be appropriate for the EFI -RCN NEON Ecological Forecast Challenge happening concurrently with -the 2021 Fish 507 course. You are welcome to do one of the challenges as -your individual project. You do not need to formally participate in the -challenge (i.e. register as a team but you are welcome to do so). All -the data are provided and the challenge lays out the goals (what to -forecast) for each challenge. The Aquatic Ecosystems, Tick Abundance, -and Beetle Abundance challenges would be appropriate for the class.

+

Each student will have to write a complete, publishable (<20 page) paper that may, or may not, serve as a component of their thesis/dissertation. Given that some students might not have their own data, students may also use data from the instructors, public datasets or datasets included in R libraries. See list of prior student projects for the types of projects done is prior years.

+

The techniques you will be learning would be appropriate for the EFI RCN NEON Ecological Forecast Challenge happening concurrently with the 2021 Fish 507 course. You are welcome to do one of the challenges as your individual project. You do not need to formally participate in the challenge (i.e. register as a team but you are welcome to do so). All the data are provided and the challenge lays out the goals (what to forecast) for each challenge. The Aquatic Ecosystems, Tick Abundance, and Beetle Abundance challenges would be appropriate for the class.

Peer reviews (20%)

-

Each student will have to provide 2 anonymous peer-reviews of their -colleagues’ papers (10% each).

+

Each student will have to provide 2 anonymous peer-reviews of their colleagues’ papers (10% each).

Participation (10%)

-

This is a graduate-level course and we expect a certain amount of -engagement from each student, which includes attending and participating -lectures and computer labs.

-

Students should discuss any potential schedule conflicts with the -instructor during the first week of class.

+

This is a graduate-level course and we expect a certain amount of engagement from each student, which includes attending and participating lectures and computer labs.

+

Students should discuss any potential schedule conflicts with the instructor during the first week of class.


Academic integrity

-

Faculty and students at the University of Washington are expected to -maintain the highest standards of academic conduct, professional -honesty, and personal integrity. Plagiarism, cheating, and other -academic misconduct are serious violations of the Student -Conduct Code. we have no reason to believe that anyone will violate -the Student Conduct Code, but we will have no choice but to -refer any suspected violation(s) to the College of the Environment for a -Student Conduct Process hearing. Students who have been guilty of a -violation will receive zero points for the assignment in question.

+

Faculty and students at the University of Washington are expected to maintain the highest standards of academic conduct, professional honesty, and personal integrity. Plagiarism, cheating, and other academic misconduct are serious violations of the Student Conduct Code. we have no reason to believe that anyone will violate the Student Conduct Code, but we will have no choice but to refer any suspected violation(s) to the College of the Environment for a Student Conduct Process hearing. Students who have been guilty of a violation will receive zero points for the assignment in question.


Mental health

-

We are in the midst of an historic pandemic that is creating a -variety of challenges for everyone. If you should feel like you need -some help, please consider the following resources available to -students.

-

If you are experiencing a life-threatening emergency, please -dial 911.

-

Crisis -Clinic
+

We are in the midst of an historic pandemic that is creating a variety of challenges for everyone. If you should feel like you need some help, please consider the following resources available to students.

+

If you are experiencing a life-threatening emergency, please dial 911.

+

Crisis Clinic
Phone: 206-461-3222 or toll-free at 1-866-427-4747

-

UW -Counseling Center
+

UW Counseling Center
Phone: 206-543-1240
-Immediate -assistance

-

Let’s -Talk

-

Hall -Health Mental Health

+Immediate assistance

+

Let’s Talk

+

Hall Health Mental Health


Safety

-

If you feel unsafe or at-risk in any way while taking any course, -contact SafeCampus -(206-685-7233) anytime–no matter where you work or study–to anonymously -discuss safety and well-being concerns for yourself or others. -SafeCampus can provide individualized support, discuss short- and -long-term solutions, and connect you with additional resources when -requested. For a broader range of resources and assistance see the Husky Health & Well-Being -website.

+

If you feel unsafe or at-risk in any way while taking any course, contact SafeCampus (206-685-7233) anytime–no matter where you work or study–to anonymously discuss safety and well-being concerns for yourself or others. SafeCampus can provide individualized support, discuss short- and long-term solutions, and connect you with additional resources when requested. For a broader range of resources and assistance see the Husky Health & Well-Being website.


Food Pantry

-

No student should ever have to choose between buying food or -textbooks. The UW Food Pantry helps mitigate the social and academic -effects of campus food insecurity. They aim to lessen the financial -burden of purchasing food by providing students access to shelf-stable -groceries, seasonal fresh produce, and hygiene products at no -cost. Students can expect to receive 4 to 5 days’ worth of -supplemental food support when they visit the Pantry, located on the -north side of Poplar Hall at the corner of NE 41st St and -Brooklyn Ave NE. Visit the Any -Hungry Husky website for additional information, including operating -hours and additional food support resources.

+

No student should ever have to choose between buying food or textbooks. The UW Food Pantry helps mitigate the social and academic effects of campus food insecurity. They aim to lessen the financial burden of purchasing food by providing students access to shelf-stable groceries, seasonal fresh produce, and hygiene products at no cost. Students can expect to receive 4 to 5 days’ worth of supplemental food support when they visit the Pantry, located on the north side of Poplar Hall at the corner of NE 41st St and Brooklyn Ave NE. Visit the Any Hungry Husky website for additional information, including operating hours and additional food support resources.


Endnotes

-

[1] If the instructor should be the one to say or do something that -makes a student uncomfortable, the student should feel free to contact -the Director of the School of Aquatic and Fishery Sciences.

+

[1] If the instructor should be the one to say or do something that makes a student uncomfortable, the student should feel free to contact the Director of the School of Aquatic and Fishery Sciences.


-This site was last updated at 05:44 on 09 Nov 2022 +This site was last updated at 11:00 on 21 Nov 2022
diff --git a/docs/lectures.html b/docs/lectures.html index c9656c90..35e172a2 100644 --- a/docs/lectures.html +++ b/docs/lectures.html @@ -13,7 +13,7 @@ Lecture presentations - + @@ -315,8 +315,7 @@

Lecture presentations

Meeting days & times

Tuesday & Thursday from 1:30-2:50

-
+
@@ -351,26 +350,16 @@

Meeting days & times

Mark
@@ -381,27 +370,16 @@

Meeting days & times

Mark
@@ -412,23 +390,16 @@

Meeting days & times

Mark
@@ -439,33 +410,16 @@

Meeting days & times

Eli
@@ -479,29 +433,13 @@

Meeting days & times

Univariate state-space models
@@ -515,33 +453,13 @@

Meeting days & times

Introduction to multivariate state-space models
@@ -555,24 +473,13 @@

Meeting days & times

MARSSX and ARMAX models
Seasonal effects
Missing covariates
@@ -583,27 +490,16 @@

Meeting days & times

Mark
@@ -617,17 +513,13 @@

Meeting days & times

Dynamic linear models (DLMs)
@@ -641,18 +533,13 @@

Meeting days & times

Hidden Markov models
@@ -666,20 +553,13 @@

Meeting days & times

Bayesian estimation of time-series and state-space models
Stan
@@ -690,13 +570,10 @@

Meeting days & times

Eric
@@ -742,33 +610,16 @@

Meeting days & times

Eli
@@ -782,26 +633,13 @@

Meeting days & times

Semi- and non-parametric models
@@ -812,22 +650,16 @@

Meeting days & times

Mark
@@ -841,20 +673,13 @@

Meeting days & times

Spatial and spatio-temporal models
@@ -868,8 +693,7 @@

Meeting days & times

Spatial and spatio-temporal models
-Course overview
Properties of time series
Data transformations -
Time series decomposition
+Course overview
Properties of time series
Data transformations
Time series decomposition
-pdf 1 pdf 2
html 1 html 2
Rmd 1 Rmd 2 +pdf 1 pdf 2
html 1 html 2
Rmd 1 Rmd 2 		Video -CM09: Chap 1 -
HA18: Chap -6
HSW18: -Intro to ts
+CM09: Chap 1
HA18: Chap 6
HSW18: Intro to ts
-Covariance & correlation
Autocorrelation & Partial -autocorrelation
Cross correlation
White noise
Random walks -
Differencing
+Covariance & correlation
Autocorrelation & Partial autocorrelation
Cross correlation
White noise
Random walks
Differencing
-pdf -1
html -1
Rmd 1 +pdf 1
html 1
Rmd 1 		Video -CM09: Chap 2 -
CM09: Chap 4 -
HA18: Chap -8.1
HSW18: -Intro to ts
+CM09: Chap 2
CM09: Chap 4
HA18: Chap 8.1
HSW18: Intro to ts
-Autoregressive (AR) models
Moving average (MA) models
Stationary -AR models
Invertible MA models
Using ACF & PACF for model ID -
+Autoregressive (AR) models
Moving average (MA) models
Stationary AR models
Invertible MA models
Using ACF & PACF for model ID
-pdf 1
html 1
Rmd 1 +pdf 1
html 1
Rmd 1 		Video -CM09: Chap 6 -
CM09: Chap 7 -
HA18: Chap 8.1-8.5 -
+CM09: Chap 6
CM09: Chap 7
HA18: Chap 8.1-8.5
-Box-Jenkins method
Fitting ARIMA models with R
Forecasting with -ARIMA models
Seasonal ARIMA models
+Box-Jenkins method
Fitting ARIMA models with R
Forecasting with ARIMA models
Seasonal ARIMA models
-pdf -1 pdf 2 -pdf 3
html -1 html -2 html -3 html -4
Rmd -1 Rmd 2 -Rmd 3 Rmd 4 +pdf 1 pdf 2 pdf 3
html 1 html 2 html 3 html 4
Rmd 1 Rmd 2 Rmd 3 Rmd 4 		-Video
Video +Video
Video 		-HA18: Chap 8.6-8.9 -
H18: -ARIMA Models
+HA18: Chap 8.6-8.9
H18: ARIMA Models
-
html -1
Rmd 1 +
html 1
Rmd 1 		Video -HWS18a: -Chap 7
HWSb: -Chap 6
MARSS -function
MARSS Ref Sheet -
uni_example_1.R -
uni_example_2.R -
uni_example_3.R -
uni_example_4.R
+HWS18a: Chap 7
HWSb: Chap 6
MARSS function
MARSS Ref Sheet
uni_example_1.R
uni_example_2.R
uni_example_3.R
uni_example_4.R
-pdf -1 pdf 2
html -1
Rmd -1 +pdf 1 pdf 2
html 1
Rmd 1 		Video -HWS18a: -Chap 8
MARSS -function
MARSS Ref Sheet -
uni_example_0.R -
marss_example_0.R -
marss_example_0_with_comments.R -
marss_example_1.R -
marss_example_2.R -
marss_example_3.R -
marss_example_4.R -
marss_example_5.R -
+HWS18a: Chap 8
MARSS function
MARSS Ref Sheet
uni_example_0.R
marss_example_0.R
marss_example_0_with_comments.R
marss_example_1.R
marss_example_2.R
marss_example_3.R
marss_example_4.R
marss_example_5.R
-pdf 1
html 1
Rmd 1 +pdf 1
html 1
Rmd 1 		Video -lec_07_covariates.R -
MARSS -Models HWS18a: Chap 8
Missing -Covariates HWS18a: Chap 11
HA18: Chap 5
HA18: Chap 9
H18: -Chap 6
+lec_07_covariates.R
MARSS Models HWS18a: Chap 8
Missing Covariates HWS18a: Chap 11
HA18: Chap 5
HA18: Chap 9
H18: Chap 6
-Regression with autocorrelated errors
Dynamic factor analysis (DFA) -
+Regression with autocorrelated errors
Dynamic factor analysis (DFA)
-pdf 1
html 1
Rmd 1 +pdf 1
html 1
Rmd 1 		Video -Zuur -et al. 2003a
Zuur et -al. 2003b
Ohlberger -et al. 2016
HWS18a: -Chap 10
+Zuur et al. 2003a
Zuur et al. 2003b
Ohlberger et al. 2016
HWS18a: Chap 10
-pdf 1
html 1
Rmd 1 +pdf 1
html 1
Rmd 1 		Video -Petris et al. (2009)
HWS18a: -Chap 16
Scheuerell & Williams (2005)
+Petris et al. (2009)
HWS18a: Chap 16
Scheuerell & Williams (2005)
-
html 1
Rmd 1 +
html 1
Rmd 1 		Video -Zucchini -et al. 2006
depmixS4 -vignette
+Zucchini et al. 2006
depmixS4 vignette
-pdf 1
html 1
Rmd 1 +pdf 1
html 1
Rmd 1 		Video -Stan manual -
Monnahan -et al. 2017
mcmc -intro
+Stan manual
Monnahan et al. 2017
mcmc intro
-MAP estimation
Multivariate models
DFA
Writing Bayesian -models in Stan
+MAP estimation
Multivariate models
DFA
Writing Bayesian models in Stan
-pdf 1
html 1
Rmd 1 +pdf 1
html 1
Rmd 1 		Video @@ -713,25 +590,16 @@

Meeting days & times

Eric
-Multi-model inference and selection
Information criteria -
Cross-validation & LOOIC
+Multi-model inference and selection
Information criteria
Cross-validation & LOOIC
-pdf 1
html 1
Rmd 1 +pdf 1
html 1
Rmd 1 		Video -See list of references in the lecture
HA18: Section 3.4 -
H18: -Forecast accuracy
H18: -Variable selection
+See list of references in the lecture
HA18: Section 3.4
H18: Forecast accuracy
H18: Variable selection
-AR(p) Models
Estimating interaction strengths
Gompertz models -
Stability metrics
+AR(p) Models
Estimating interaction strengths
Gompertz models
Stability metrics
-
html -1
Rmd -1 +
html 1
Rmd 1 		Video -Hampton -et al 2013
Ives -et al 2003
HWS18a: -Chap 14 & 18
MAR -Stability Metrics
AR(1) -errors
Mark’s -B estimation talk
+Hampton et al 2013
Ives et al 2003
HWS18a: Chap 14 & 18
MAR Stability Metrics
AR(1) errors
Mark’s B estimation talk
-pdf 1
html 1
Rmd 1 +pdf 1
html 1
Rmd 1 		Video -Chang -et al. 2017
Sugihara and May -
rEDM -Vignette
Intro to EDM, -video
EDM -lecture
EDM -for beginners, review
+Chang et al. 2017
Sugihara and May
rEDM Vignette
Intro to EDM, video
EDM lecture
EDM for beginners, review
-Frequency domain
Fourier transforms
Spectral analysis -
Wavelet analysis
+Frequency domain
Fourier transforms
Spectral analysis
Wavelet analysis
-pdf 1
html 1
Rmd 1 +pdf 1
html 1
Rmd 1 		Video -Graps -(1995)
Percival & Walden -(2000)
+Graps (1995)
Percival & Walden (2000)
-
html -1
Rmd 1 +
html 1
Rmd 1 		Video -River -spatial models
River -spatial 2
River -spatial 3
+River spatial models
River spatial 2
River spatial 3
-
html -1
Rmd 1 +
html 1
Rmd 1 		Video @@ -889,8 +713,7 @@

Meeting days & times

Regression versus State Space
-
html 1
Rmd 1 +
html 1
Rmd 1 		Video diff --git a/docs/list_of_labs.html b/docs/list_of_labs.html index e887c852..1a57c771 100644 --- a/docs/list_of_labs.html +++ b/docs/list_of_labs.html @@ -13,7 +13,7 @@ List of Labs - + diff --git a/docs/list_of_lectures.html b/docs/list_of_lectures.html index 8c3ad16b..87a7b311 100644 --- a/docs/list_of_lectures.html +++ b/docs/list_of_lectures.html @@ -13,7 +13,7 @@ List of Lectures - + diff --git a/docs/references.html b/docs/references.html index 040109c4..d2ad69c1 100644 --- a/docs/references.html +++ b/docs/references.html @@ -13,7 +13,7 @@ References - + @@ -308,127 +308,49 @@

References

-
+

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

-

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.

-

HWS18a: Holmes EE, Ward EJ, Scheuerell MD. 2014. Analysis of -Multivariate Time Series Using the MARSS Package. Available -here

-

HA18: Hyndman RJ, Athanasopoulos G. 2018. Forecasting: Principles and -Practice. eBook. Available -here

-

H18: Holmes, E. E. Fisheries Catch Forecasting with R. 2018. eBook. -Available -here

+

HSW18b: Holmes, E. E., M. D. Scheuerell, and E. J. Ward. Applied Time Series Analysis for Fisheries and Environmental data. eBook. Available here

+

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.

+

HWS18a: Holmes EE, Ward EJ, Scheuerell MD. 2014. Analysis of Multivariate Time Series Using the MARSS Package. Available here

+

HA18: Hyndman RJ, Athanasopoulos G. 2018. Forecasting: Principles and Practice. eBook. Available here

+

H18: Holmes, E. E. Fisheries Catch Forecasting with R. 2018. eBook. Available here

-
+

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.

+

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.

+

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.

diff --git a/docs/review_guide.html b/docs/review_guide.html index c1f3849b..8e3eee34 100644 --- a/docs/review_guide.html +++ b/docs/review_guide.html @@ -13,7 +13,7 @@ Review Guidelines - + @@ -308,36 +308,20 @@

Review Guidelines

-

As part of your grade (20% total; 10% for each review), each student -provide 2 anonymous peer-reviews of their colleagues’ papers. A template -to use for your review is below.

+

As part of your grade (20% total; 10% for each review), each student provide 2 anonymous peer-reviews of their colleagues’ papers. A template to use for your review is below.

Advice on reviewing scientific papers

-

If you are looking for some guidance on writing reviews of scientific -papers, here are some links to various columns, blogs, etc, about -reviewing scientific papers:

-

Arthropod -Ecology

-

Violent -metaphors (don’t let the name scare you)

-

Duke -Writing Lab

-

Examples -of good reviews from Peerage

+

If you are looking for some guidance on writing reviews of scientific papers, here are some links to various columns, blogs, etc, about reviewing scientific papers:

+

Arthropod Ecology

+

Violent metaphors (don’t let the name scare you)

+

Duke Writing Lab

+

Examples of good reviews from Peerage

Review Guidelines

-

Final papers will be peer-reviewed and reviewed by instructors based -on the following criteria. Please read before you submit your final -paper.

-

Example -of a manuscript review for Fish 507.docx

-

Review templates: MS Word or -R Markdown

+

Final papers will be peer-reviewed and reviewed by instructors based on the following criteria. Please read before you submit your final paper.

+

Example of a manuscript review for Fish 507.docx

+

Review templates: MS Word or R Markdown

diff --git a/docs/site_libs/header-attrs-2.14/header-attrs.js b/docs/site_libs/header-attrs-2.13/header-attrs.js similarity index 100% rename from docs/site_libs/header-attrs-2.14/header-attrs.js rename to docs/site_libs/header-attrs-2.13/header-attrs.js diff --git a/docs/site_libs/navigation-1.1/codefolding-lua.css b/docs/site_libs/navigation-1.1/codefolding-lua.css deleted file mode 100644 index 183b19e1..00000000 --- a/docs/site_libs/navigation-1.1/codefolding-lua.css +++ /dev/null @@ -1,9 +0,0 @@ -detaiks.chunk-details > summary.chunk-summary { - text-align: right; -} -details.chunk-details[open] > summary.chunk-summary::after { - content: "Hide"; -} -details.chunk-details[open] > summary.chunk-summary > span.chunk-summary-text { - display: none; -} diff --git a/docs/site_libs/navigation-1.1/codefolding.js b/docs/site_libs/navigation-1.1/codefolding.js index 97fffb29..981faea1 100644 --- a/docs/site_libs/navigation-1.1/codefolding.js +++ b/docs/site_libs/navigation-1.1/codefolding.js @@ -19,8 +19,6 @@ window.initializeCodeFolding = function(show) { // select all R code blocks var rCodeBlocks = $('pre.r, pre.python, pre.bash, pre.sql, pre.cpp, pre.stan, pre.julia, pre.foldable'); rCodeBlocks.each(function() { - // skip if the block has fold-none class - if ($(this).hasClass('fold-none')) return; // create a collapsable div to wrap the code in var div = $('
'); diff --git a/docs/student_pubs.html b/docs/student_pubs.html index 4dae442f..a54f7d3a 100644 --- a/docs/student_pubs.html +++ b/docs/student_pubs.html @@ -13,7 +13,7 @@ Student Projects - + @@ -308,122 +308,78 @@

Student Projects

-

Here are the independent projects from students who took the class in -prior years.

+

Here are the independent projects from students who took the class in prior years.

Presentation topics

-

Here are the topics of the students’ final projects from years -past.

+

Here are the topics of the students’ final projects from years past.

2021

    -
  • “Heat-Related Mortality in the Greater Seattle Area (1980 - -2018)”
    • -
  • “Modeling spatio-temporal dynamics of mountain pine beetle activity -in the Southern Rockies”
    • -
  • “Quantifying the success of invasive green sunfish eradication from -McGee Wash, Arizona”
    • -
  • “Recovery of the California brown pelican following DDT -contamination in the Southern California Bight”
    • +
  • “Heat-Related Mortality in the Greater Seattle Area (1980 - 2018)”
    • +
  • “Modeling spatio-temporal dynamics of mountain pine beetle activity in the Southern Rockies”
    • +
  • “Quantifying the success of invasive green sunfish eradication from McGee Wash, Arizona”
    • +
  • “Recovery of the California brown pelican following DDT contamination in the Southern California Bight”
  • “Mobile technologies reveal human activity across urban lakes”
    • -
  • “Population viability analysis of marbled murrelets reveal long-term -decline in the Salish Sea”
    • -
  • “Using removal data to estimate rusty crayfish population growth and -spread in the John Day River”
    • -
  • “Synchronous dynamics in marine fishes in the Southern California -Bight”
    • +
  • “Population viability analysis of marbled murrelets reveal long-term decline in the Salish Sea”
    • +
  • “Using removal data to estimate rusty crayfish population growth and spread in the John Day River”
    • +
  • “Synchronous dynamics in marine fishes in the Southern California Bight”

2019

    -
  • “A century of stable isotope data demonstrates robust trophic -ecology and ecosystem productivity from archival bone of a generalist -predator”
    • -
  • “Estimating common trends in bull trout life history -strategies”
    • -
  • “Assessing spatial covariance among time series of river discharge -across western North America”
    • -
  • “Timing and abundance of juvenile spring Chinook emigrants from the -Chiwawa River”
    • +
  • “A century of stable isotope data demonstrates robust trophic ecology and ecosystem productivity from archival bone of a generalist predator”
    • +
  • “Estimating common trends in bull trout life history strategies”
    • +
  • “Assessing spatial covariance among time series of river discharge across western North America”
    • +
  • “Timing and abundance of juvenile spring Chinook emigrants from the Chiwawa River”
  • “Cetacean Strandings in the Pacific Northwest”

2017

    -
  • “Trends and shortcomings in midwinter waterfowl surveys along the -Pacific Flyway: a multivariate state-space modeling approach”
    • -
  • “Cyclic dominance of alternative male phenotypes in an introduced -sockeye salmon population”
    • -
  • “An application of arima model to study the association between -particulate matters and hospital admission for respiratory disease”
    • -
  • “Time resolved exposures of mice to cadmium: hair as a biomarker of -exposure”
    • -
  • “Characterizing temporal variability in pelagic communities for -biological monitoring at marine renewable energy sites”
    • -
  • “Lake size and watershed characteristics mediate the effect of -climate on lake water levels in the Puget Sound Lowlands”
    • -
  • “Determining multi-scale controls on river temperature: a time -series approach”
    • +
  • “Trends and shortcomings in midwinter waterfowl surveys along the Pacific Flyway: a multivariate state-space modeling approach”
    • +
  • “Cyclic dominance of alternative male phenotypes in an introduced sockeye salmon population”
    • +
  • “An application of arima model to study the association between particulate matters and hospital admission for respiratory disease”
    • +
  • “Time resolved exposures of mice to cadmium: hair as a biomarker of exposure”
    • +
  • “Characterizing temporal variability in pelagic communities for biological monitoring at marine renewable energy sites”
    • +
  • “Lake size and watershed characteristics mediate the effect of climate on lake water levels in the Puget Sound Lowlands”
    • +
  • “Determining multi-scale controls on river temperature: a time series approach”

2015

    -
  • “Modeling domoic acid in Pacific razor clams (Siliqua -patula) at Long Beach, WA, USA using autoregressive integrated -moving average and generalized linear models”
    • -
  • “Synthesis of Bristol Bay sockeye salmon genetic data improves -understanding of migration patterns to inform commercial fisheries -management”
    • -
  • “Behavior of depth selection in lake trout (Salvelinus -namaycush) morphotypes in northern Lake Superior”
    • -
  • “Extracting time-varying climate-driven growth index for use in -stock assessment models”
    • -
  • “A comparison of statistical models applicable for environmental -assessments of marine renewable energy sites”
    • -
  • “A method of reconstructing the daily growth of large wildfires with -state space models”
    • -
  • “Modeling and forecasting multispecies catches in the Uruguayan -longline fishery”
    • -
  • “Chinook salmon jack rate in the Columbia River basin: are jacks -trending?”
    • -
  • “Spatial asynchrony in juvenile sockeye salmon growth driven by -competition in a changing climate”
    • +
  • “Modeling domoic acid in Pacific razor clams (Siliqua patula) at Long Beach, WA, USA using autoregressive integrated moving average and generalized linear models”
    • +
  • “Synthesis of Bristol Bay sockeye salmon genetic data improves understanding of migration patterns to inform commercial fisheries management”
    • +
  • “Behavior of depth selection in lake trout (Salvelinus namaycush) morphotypes in northern Lake Superior”
    • +
  • “Extracting time-varying climate-driven growth index for use in stock assessment models”
    • +
  • “A comparison of statistical models applicable for environmental assessments of marine renewable energy sites”
    • +
  • “A method of reconstructing the daily growth of large wildfires with state space models”
    • +
  • “Modeling and forecasting multispecies catches in the Uruguayan longline fishery”
    • +
  • “Chinook salmon jack rate in the Columbia River basin: are jacks trending?”
    • +
  • “Spatial asynchrony in juvenile sockeye salmon growth driven by competition in a changing climate”

2013

    -
  • “Can distinct sub-populations within the Prince William Sound be -classified by differing responses in male fecundity and do oceanographic -conditions affect surveys of male fecundity?”
    • +
  • “Can distinct sub-populations within the Prince William Sound be classified by differing responses in male fecundity and do oceanographic conditions affect surveys of male fecundity?”
  • “Scales of variability in forage fish stocks”
    • -
  • “Extreme weather incidents and prey availability affect Magellanic -penguin (Spheniscus magellanicus) reproductive success”
    • -
  • “Environment drives changes in recruitment for most marine -fisheries”
    • -
  • “Initial exploration of modeling options for an environmental driver -of sardine abundance”
    • +
  • “Extreme weather incidents and prey availability affect Magellanic penguin (Spheniscus magellanicus) reproductive success”
    • +
  • “Environment drives changes in recruitment for most marine fisheries”
    • +
  • “Initial exploration of modeling options for an environmental driver of sardine abundance”
  • “Demographic structure of Puget Sound Pacific herring stocks”
    • -
  • “Common trends in spawning stock biomass of northeast pacific marine -stocks”
    • -
  • “Describing variability of fish and macrozooplankton density at -marine hydrokinetic energy sites”
    • -
  • “Lake specific variation in juvenile sockeye salmon growth driven by -competition in a changing climate”
    • -
  • “Environmental and phenological variability: match-mismatch effects -of lake conditions and entry timing on sockeye salmon survival and -growth”
    • +
  • “Common trends in spawning stock biomass of northeast pacific marine stocks”
    • +
  • “Describing variability of fish and macrozooplankton density at marine hydrokinetic energy sites”
    • +
  • “Lake specific variation in juvenile sockeye salmon growth driven by competition in a changing climate”
    • +
  • “Environmental and phenological variability: match-mismatch effects of lake conditions and entry timing on sockeye salmon survival and growth”

Publications

-

Siple, M. C. and Francis, T. B. 2016. Population diversity in Pacific -herring of Puget Sound, USA. Oecologia 180: 111-125.Link to article

+

Siple, M. C. and Francis, T. B. 2016. Population diversity in Pacific herring of Puget Sound, USA. Oecologia 180: 111-125.Link to article

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