Course description:

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. 

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:

  1. A very approachable primer of population parameter estimation:  http://larkinpowell.wixsite.com/larkinpowell/estimation-of-parameters-for-animal-pop
  2. Michael Conroy's course website: https://sites.google.com/site/cmrsoftware/home
  3. Jay Rotella's course website: http://www.montana.edu/rotella/502/Schedule.html
  4. MARK program help book, includes info on analysis: http://www.phidot.org/software/mark/
  5. Good intro to R for wildlife students by USGS: https://sites.google.com/site/rforfishandwildlifegrads/

And many more...

  

Recommended texts:

  1. Skalski, John R., Kristin E. Ryding, and Joshua Millspaugh. Wildlife demography: analysis of sex, age, and count data. 2005.
  2. Williams, Byron K., James D. Nichols, and Michael J. Conroy. Analysis and management of animal populations. 2002.
  3. 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.

  

Schedule:

Grading:

Grades will be based on weekly participation in coding exercises and code-sharing from previous coding exercises (10 pts per weekly meeting).