Course Syllabus

Lectures: MWF 10:30-11:20am.  Short videos totaling about 30 min will be posted in advance of each lecture, recorded using Panopto. Students will need to answer one or more questions online using PollEverywhere to demonstrate they have listened to the videos. The instructor will be on a Zoom session answering questions throughout the scheduled lecture period. All students should be on Zoom from 11:00-11:20am  during which time the instructor will be leading discussions. 

Labs: Th 1:00-2:50pm. Short videos recorded using Panopto will be posted in advance giving a demo of each part of the lab.  The instructor will be on a Zoom session answering questions throughout the scheduled lab period. Students will be required to submit their workings (even if incomplete) within 1 hr of the end of the lab for credit. 

Instructor: Trevor A. Branch, tbranch@uw.edu. Emails will be answered quickly and are preferred to questions posed on Canvas. 
Office hours: by appointment. 
Grader: John Trochta, johnt23@uw.edu

Learning objectives

• Use advanced Excel commands (pivot tables, what-if scenarios using the Table function, using Solver to fit models to data).
• Be proficient in R programming, including the use of loops, functions, and fitting models to data.
• Understand a variety of population dynamics models including models of total numbers and biomass for whales, age-structured models of elephants and wildebeest, and stock-recruitment models.
• Find maximum sustainable yield for fisheries.
• Evaluate extinction risk and how this is affected by the number of populations and by depensation (Allee effect) at low population sizes.
• Build spatial models to evaluate the effect of protected areas on fisheries catch, profit, and costs.
• Fit non-linear maximum likelihood models to multiple datasets to estimate sustainable yield and assess population status, and find 95% confidence intervals for model parameters.
• Program and solve Bayesian models in R by coding up your own Markov-chain-Monte-Carlo (MCMC) algorithm, and use the results to estimate parameter values, assess population status, and evaluate the effects of alternative management policies.
• Understand the concepts of harvest control rules and management strategy evaluations, that test the effects of alternative policy choices.

Course outline

Software skills

Advanced Excel instruction: pivot tables, what-if scenarios using the Table function, solver to fit models to data
R instruction: programming skills including the use of loops, functions, and fitting models to data

Models

Models of total numbers and biomass
Age-structured models
Stock-recruitment models (generation-to-generation models)
Finding maximum sustainable yield for fisheries
Models of low density dynamics (extinction risk, depensation)
Spatial models

Fitting models to data

Maximum likelihood estimation
Finding confidence intervals using likelihood profiles
Bayesian models using MCMC and SIR

Policy evaluation

Calculating extinction risk
Optimal harvesting: estimating maximum sustainable yield
Impact of marine reserves on fish catches and biodiversity
Forward projection from Bayesian model output
Harvest control rules
Management strategy evaluation

Prerequisites

Introduction to Ecological Modeling FISH 454 and Q SCI 482 or the equivalent is recommended. This course includes instruction on how to program in R, which is a highly marketable workplace skill, and it will be advantageous to have familiarity with the statistical programming language R, or to have taken Introduction to R FISH552 and Advanced R Programming FISH553. Instruction in the first lab covers the basics of R programming needed for the course: for-loops, writing functions, calling functions from other functions, using vectors and matrices, if-then-else statements, reading and writing .csv files, and producing basic line plots and histograms. Lectures and labs will be run in Excel and in R using Rstudio. Other R editors may be used. 

Textbook

There are no required textbooks, although the following two books are useful. The instructor has several extra copies but given social distancing can't easily distribute these:

"The Ecological Detective" by Hilborn and Mangel, which is an easy-reading and useful general reference written for ecologists about how to fit maximum likelihood and Bayesian models to data (the core part of the course).

"The Art of R Programming" by Matloff, which is an R programming textbook that will serve you in this class and well beyond. A draft version can be found here for free. The key chapters needed for the class are chapters 1-4, 8-9 and 11. Participants are advised to read through these chapters before and during the course if they are not familiar with R.

Time commitment

Watching lecture videos and participating in group lecture sessions; watching lab videos and submitting lab work online (5 hr per week). 
Two midterm examinations (10 hr preparation time). These will be 50-minute open-book midterms during scheduled lecture times on Wednesday 6 May and  Friday 5 June that test knowledge of materials from lectures, readings and labs. 
There is no final examination for this class.
The lab exam during the scheduled lab time on Thursday 4 June (10 hr preparation time) will be an open book two-hour lab exam. Graduate students are required to take the exam in R; undergrads can choose Excel or R. The exam will test your practical ability to create models and fit them to data.
Homework (4-8 hr per week): There will be 7 project-style homework problems assigned, initially every week (in Excel), then every two weeks (in R). The first assignment is due Thursday 9 April at 9pm; the final assignment is due on Wednesday 10 June at 9pm during exam week. 

Grading

A percentage grade will be assigned for the following components of the course, with highest weight given to the homeworks: 
15% Mid-term I
15% Mid-term II
15% Lab exam
50% Homework (5% each for first four one-week assignments; 10% each for last three two-week assignments)
5% In-class quizzes and lab handins (participation required for credit)
Grades are not converted using a curve, thus everyone can do well in the class. Instead, percentages are converted to a grade on the point scale (0.7-4.0) as follows: I pick a lower bound for a 0.7 score, usually 30-50%, and an upper bound for a 4.0 score (usually 90-95%), then linearly interpolate between these points. For example, if the lower bound is 40% and the upper bound is 95%, then the percentages are converted to grades as follows: 
<40% 0.0
40% 0.7
50% 1.3
60% 1.9
70% 2.5
80% 3.1
90% 3.7
>=95% 4.0

University policy on plagiarism and misconduct

Plagiarism, cheating, and other misconduct are serious violations of the student conduct code. You should know and follow the UW's policies on cheating and plagiarism. Any suspected cases of academic misconduct will be handled according to UW regulations. More information, including definitions and examples, can be found in the Faculty Resource for Grading and the Student Conduct Code (WAC 478-121).

Religious accommodations

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy (https://registrar.washington.edu/staffandfaculty/religious-accommodations-policy/). Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form (https://registrar.washington.edu/students/religious-accommodations-request/).

Panopto, Zoom, and privacy of recordings

The first 30 min of each lecture will be pre-recorded in a series of short videos using Panopto, and the last 20 min (starting at 11:00am) will be run synchronously at our scheduled class time via Zoom. These Zoom class sessions will be recorded. The recording will capture the presenter’s audio, video and computer screen. Student audio and video will be recorded if they share their computer audio and video during the recorded session. The recordings will only be accessible to students enrolled in the course to review materials. These recordings will not be shared to the public, and will be deleted after the course ends.

UW-IT and Zoom have a Business Associates Agreement (BAA) to protect the security and privacy of UW Zoom accounts and is FERPA compliant. Students who do not wish to give consent to being recorded should:

• Choose a Zoom username that does not include any personal identifying information like their name or UW Net ID
• Never share their computer audio or video during their Zoom sessions

By enrolling in this class, all students agree to never upload the recordings to other platforms.