• Lecture: Tu/Th 3:40 - 4:55, 1104 Gilman
• Instructor: Dr. Philip Dixon, pdixon at iastate dot edu, 2121 Snedecor, 4-2142
• TA: Jing Hou jinghou at iastate dot edu
• Office Hours: Weds, 4-5pm, in 2102 Snedecor (medium conference room)
• Review / Help: during office hours or set up a time to meet with me or Jing
  
• Questions?: Please feel free to e-mail me anytime (pdixon at iastate.edu) with questions or comments.
• Class Web site: https://pdixon.stat.iastate.edu/stat534
  Copies of the syllabus, homework assignments, data sets and example programs will be available here.
• Text: Borchers, Buckland and Zucchini, 2002. Estimating Animal Abundance, Closed Populations. Springer
  A copy will be on reserve at the Library.
• Reading materials: Various other readings will be available in the Statistics Dept. Office. Visit the front desk to borrow the material to make a copy. I will provide a copy to the NREM students to share.
• Learning Outcomes: By the end of the course, students will be able to:
  • Use statistical methods to answer ecological questions, focusing on population ecology
  • Understand the statistical theory for those methods.
  • Apply classical and modern statistical inference to some non-standard problems.
  • Apply statistical methods and theory to novel problems.
• Grading:
  4 Homework sets: 50 points each.
  2 Take home exams: 100 points each.
  Small group project: 100 points

  Homework and exam answers are due at 5pm on the assigned date. If a late submission is anticipated, I hope you will contact me before hand.

• Computing:
  This course emphasizes data analysis to answer questions. This will require using statistical computing packages. Unfortunately, no one package does everything well. You may use whatever tools are appropriate to do the necessary computations. I expect that most of you will use R; I provide help with R. In particular, I will distribute examples of R code on the class web site. Some of you may be familiar with specialized programs developed for certain parts of the course (e.g. Program MARK for estimation of population parameters). You are free to use that program, but I will not lecture about it.

 
Weeks  Dates         	Topic

 1-2   Aug 21-Sep 1    Model-based analysis of species composition data
			Estimation for non-normal distributions
			Asymptotic and permutation-based inference
			
	Sep 7		HW 1 due, on week 1 and 2 material	
			   
 3-7   Sep 6 - Oct 6    Estimation of population characteristics
		       (population size, survival, detectability)
		       Mark recapture and related methods
		
	Sep 28		HW 2 due, on week 3-5 material
		
	Oct 12		Exam 1 due, on weeks 1-5 material
		                       
 8-10  Oct 9 - Oct 27  Population modeling
		       Matrix models
		       Integral projection modeling
		       Environmental variation and Population Viability Analysis
			   
	Oct 19		HW 3 due, on weeks 6-8 material
	   
	Nov 2		HW 4 due, on weeks 9-10 material

11-13  Nov 1-Nov 19   Combining models and data
			Bayesian hierarchical modeling
			Models with density dependence
			Integrated population modeling
			Model-based clustering of species composition data
		
	Nov 16		Exam 2 due, on weeks 6-10
			   
	Nov 23-27 	Thanksgiving break, no classes
                       
14-15  Nov 29-Dec 10 	Catch up, topics determined by class interest

15          		Project presentations

Finals	Dec 13-17	Project presentations, no final exam
week

Possible topics for the last two weeks:

• Your suggestions, especially methods or problems specific to your research
• Extensions of topics already discussed.
• Spatially Explicit Capture recapture
• Passive tags (genetics, hair snares, camera traps)
• Behavioural statistics, especially analysis of event sequences.
• Resource selection / habitat preference analysis
• Home range estimation and related issues

Homework: Statistics is best learnt by doing. The homework problems are chosen to give you practice in using the methods, interpreting the results, and understanding the theory. The intent is to understand and be able to apply lecture concepts, so discussion with friends and classmates is encouraged. However, you must write up your own solutions. Copying papers is not a good way to learn and will not be tolerated.

Exams: Two midterm exams will provide a second opportunity to demonstrate your knowledge of class material. Some questions will be similar to homework questions; some will be new. You must work individually on the exam questions. This includes writing your own code. You may start with code written to answer HW questions (either from your group or in my HW answers), but each of you must write your own modifications to answer exam questions.

Project: You will identify a question and data set, construct a model for those data, fit that model, and answer the question. This will be done in small groups, e.g., 2 biologists and a statistician. You choose a problem, figure out an appropriate model, fit that model and use the results to answer your motivating question. Then present your work to the class. These presentations will occur during the last week of classes and the final exam time. No written document is required.

Syllabus statements: The Board of Regents's required statement on free expression is:
Iowa State University supports and upholds the First Amendment protection of freedom of speech and the principle of academic freedom in order to foster a learning environment where open inquiry and the vigorous debate of a diversity of ideas are encouraged. Students will not be penalized for the content or viewpoints of their speech as long as student expression in a class context is germane to the subject matter of the class and conveyed in an appropriate manner.
Other syllabus statements on academic dishonesty, disability accomodation, dead week, harassment and discrimination, religious accomodation, and contact information for academic issues are in Syllabus statements.docx.

Details specific to Stat 534:
Academic dishonesty: On homework assignments: I encourage you to help each other interpret the homework problems, write code, debug code, and interpret the output. You may share code, but I encourage you to understand that code even if you didn't write it. I do require you to write your answers in your own words.
You are to work individually on the exam questions. That includes writing your own code. You are welcome to start with the code I provide in HW answers, but you can not use code written for the exam questions by another student. Exam questions will be similar but not identical to questions from the homework.
Prep week: Presentations on your hierarchical modeling project will be scheduled during prep week and the regularly scheduled final exam period. There will be no traditional exam during finals week.