This 2-credit course, taught at the University of Idaho, will cover core concepts in population analysis for wildlife biology, although it is by no means comprehensive. The course is structured around hands-on coding exercises that will allow students to grow a basic toolbox of population analytical techniques, as well as the skills to code such analyses rather than simply execute other people's pre-written R code. It will build on foundation courses in population biology, which all students should have completed, as well as basic statistical concepts. We will cover analyses related to modeling vital rates, population growth, abundance, and density.
Again, this course is NOT comprehensive- there are so many analyses these days across population biology, and we will only be covering a handful. However, major types of analyses for different kinds of data will be covered during lecture, and an important version of that type of analysis then assigned as a coding exercise for that week.
Useful websites and online texts:
A very approachable primer of population parameter estimation: http://larkinpowell.wixsite.com/larkinpowell/estimation-of-parameters-for-animal-pop
Michael Conroy's course website: https://sites.google.com/site/cmrsoftware/home
Jay Rotella's course website: http://www.montana.edu/rotella/502/Schedule.html
MARK program help book, includes info on analysis: http://www.phidot.org/software/mark/
Good intro to R for wildlife students by USGS: https://sites.google.com/site/rforfishandwildlifegrads/
A good place to look for upcoming workshops: http://www.phidot.org/forum/viewforum.php?f=8
And many more...
Skalski, John R., Kristin E. Ryding, and Joshua Millspaugh. Wildlife demography: analysis of sex, age, and count data. 2005.
Williams, Byron K., James D. Nichols, and Michael J. Conroy. Analysis and management of animal populations. 2002.
Doak, Dan, and Bill Morris. Quantitative conservation biology. 2002.
There are a huge number of books on specialty topics, from matrix models to survival, occupancy, capture-recapture, and integrated population models, and I encourage you to find and purchase those that are most beneficial to your research and learning.
Week 1: August 27th: Introduction to population analysis
Readings and resources:
Chapter 1 of Skalski et al. Book (for this weeks’ coding challenge)
To Do if needed for next week: Install program Mark, and package RMark
Week 2: September 3: Survival analysis: CJS and MLE
Week 8: October 15: SCR guest lecture with Clayton Lamb
Week 9: October 22: No class, Sophie at conference. Complete week 8 assignment, and SCR reading (TBD)
Week 10: October 29:Occupancy Analysis
Week 11: November 5: Distance Sampling
Week 12: November 12: No class, Sophie in the field. Complete Bayesian readings
Week 13: November 19: Intro to Bayesian methods. Install JAGS on your computer ASAP.
Week 14: November 26: No class, fall break
Week 15: Dec 3: Integration of analytical techniques, IPMs (guest lecture Jon Horne, IDF&G)
Week 16: Dec 7: Paper discussion and course feedback form/discussion.
Week 16: Dec 14: No class, finals week
Grades are pass/fail, and will be based on weekly participation, including coding exercises and code-sharing from previous coding exercises. Given different experience levels, a truly honest effort given your skill level is what I’m looking for (10 pts per weekly meeting). Please let me know if you will be absent for fieldwork, conference travel, etc., and I will not count that week’s participation against you.