WILD7930

HABITAT SELECTION, USE, AND OCCUPANCY

  1. Instructors:

Dr. Todd D. Steury

Office: 2347 Forestry and Wildlife Building

Office Hours: by appointment or any time my door is open

Office Phone: 844-9253

E-mail –

Web-site:

  1. Pre-requisite

STAT 7000 or similar statistics course that covers single factor ANOVA and regression

  1. Course Format

The course will consist of two 2-hour lecturesfollowed by two 2-hour labsper week (for 3 total semester credits).Lectures will involve combinations of teacher provided instruction and class discussions. Labs will involve worked examples on the computer, and in-class assignments.

  1. Text

No one text covers all the techniques that we might want to cover, Thus, much of the material will be compiled from the relevant books and the primary literature (e.g., Ecology, Journal of Wildlife Management, Conservation Biology, Frontiers in Ecology and the Environment, etc.). However, two books that would be highly useful for the course are:

Manly, B.F.J., L.L. McDonald, D.L. Thomas, T.L. McDonald, and W.P. Erickson. 2002. Resource selection by animals: statistical design and analysis for field studies. Kluwer Academic Press, Dordrecht, The Netherlands.

MacKenzie, D.I., J.D. Nichols, J.A. Royle, K.H. Pollock, L.L. Bailey, and J.E. Hines. 2006. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press, Burlington, Massachusetts.

  1. Course Description

Training in the design and analysis of field studies on the relationships between animals’ distributions in space and habitat attributes. Specific topics will include sampling design, statistical analyses, interpretation, and assumptions/limitations. Course will emphasize model building and evaluation.

  1. Course Objectives
  1. Familiarize the student withthe statistical procedures commonly used to assess relationships between animal habitat use and habitat attributes. Analytical techniques will cover use/availability data, use/non-use data, and use/non-use data when non-use is measured imperfectly (i.e., imperfect detection).
  1. Familiarize the student with the advantages and disadvantages of different data types, as well as pitfalls associated with interpretation.
  1. Train the student how to perform habitat analysis using various software such as MYSTAT, MS-Excel, the computing language R, and the package PRESENCE.
  1. Course Requirements/Evaluation

Students will be evaluated viaassignments, quizzes, and a final exam. Assignments will include weekly reportsdetailing procedures learned and performed on example data and will count 50% of the total grade. The final exam, which will be comprehensive, will count for 30% of the total grade. The remaining 20% of the total grade will come from quizzes. Quizzes and exams will be short answer and multiple choice. The standard grading scale will be used (i.e., 90-100% = A; 80-90% = B; 70-80% = C; 60-70% = D; <60% = F).

  1. Course Policies

Students are expected to attend all lectures and labs.

  1. Academic Honesty

Students should become familiar with the Student Academic Honesty Code that is published in the latest version of the Tiger Cub. Students in this class are expected to strictly adhere to this code, and any violations of the code will be brought before the Academic Honesty Committee.

  1. Justification for Graduate Courses

This is an advanced course, requiring a solid understanding of basic statistics procedures. The prerequisite for this course would be STAT 7000or similar. Students will learn to model habitat use at a level equal to that used in ecological literature. Thus, the difficulty of the topics combined with the depth with which students will learn to apply them will ensure that graduate level credit is warranted.

  1. Students with Disabilities

Students who need special accommodations in class, as provided for by the American Disabilities Act, should arrange a confidential meeting with the instructor during office hours the first week of classes- or as soon as possible if accommodations are needed immediately. You must being a copy of your Accommodation Memo and an Instructor Verification Form to the meeting. If you do not have these forms but need accommodations, make an appointment with the Program for Students with Disabilities, 1244 Haley Center, 844-2096 (V/ TT) or e-mail: .

Lecture Outline (Tues-Thurs Schedule – 3 hours each day)

Date / Lecture / Lecture Topics
06/29 / 1 / Lecture: Course introduction; syllabus review; what is habitat use and selection?
Lab: Introduction to used statistical packages
07/1 / 2 / Lecture: The hierarchy of habitat use and selection; types of use data; sampling
Lab: Introduction to general linear modeling
Reading: Johnson 1980, Buskirk and Millspaugh 2006
07/6 / 3 / Lecture: Analysis of use/non-use data
Lab: Introduction to logistic regression
Reading:
07/8 / 4 / Lecture: Estimating habitat selection from utilization distributions
Lab: Advanced topics in logistic regression
Reading: Marzluff et al. 2004, Millspaugh et al. 2006
7/13 / 5 / Lecture: Analysis of use/availability data
Lab: Introduction to Poisson regression
Reading: Keating and Cherry 2004, Johnson et al. 2006
7/15 / 6 / Lecture: Evaluation and Interpretation of RSFs/RSPFs
Lab: Inference from logistic regression and Poisson regression
Readings: Boyce et al. 2002
7/20 / 7 / Lecture: Occupancy – analysis of use/non-use with imperfect detectability
Lab: Maximum likelihood estimating
Readings: MacKenzie et al. 2002
7/22 / 8 / Lecture: Occupancy analysis continued
Lab: Occupancy analysis in R
Readings: MacKenzie et al. 2005
7/27 / 9 / Lecture: Occupancy over multiple seasons
Lab: Advanced topics in occupancy analysis
Reading:
7/29 / 10 / Lecture: Estimating population size from use/non-use data
Lab: Estimating population size
Reading: Boyce and McDonald 1999; Royle and Nichols 2003

Bibliography

Boyce, M.S., and L.L. McDonald. 1999. Relating populations to habitats using resource selection functions. Trends in Ecology and Evolution 14:268-272.

Boyce, M.S., P.R. Vernier, S.E. Nielsen, and F.K.A. Schmiegelow. 2002. Evaluating resource selection functions. Ecological Modeling 157:281-300.

Buskirk, S.W., and J.J. Millspaugh. 2006. Metrics for studies of resource selection. Journal of Wildlife Management 70:358-366.

Johnson, C.J., S.E. Nielsen, E.H. Merrill, T.L. McDonald, and M.S. Boyce. 2006. Resource selection functions based on use-availability data: theoretical motivation and evaluation methods. Journal of Wildlife Management 70:347-357.

Johnson, D.H. 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61:65-71.

Keating, K.A., and S. Cherry. 2004. Use and interpretation of logistic regression in habitat selection studies. Journal of Wildlife Management 68:774-789.

MacKenzie, D.I., J.D. Nichols, G.B. Lachman, S. Droege, J.A. Royle, and C.A. Langtimm. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248-2255.

MacKenzie, D.I., L. L. Bailey, J.D. Nichols. 2004. Investigating species co-occurrence patters when species are detected imperfectly. Journal of Animal Ecology 73:546-555.

MacKenzie, D.I., J.D. Nichols, N. Sutton, K. Kawanishi, and L.L. Bailey. 2005. Improving inferences in population studies of rare species that are detected imperfectly. Ecology 86: 1101-1113.

Marzluff, J.M., J.J. Millspaugh, P. Hurvitz, and M.S. Handcock. 2004. Relating resources to a probabilistic measure of space use: forest fragments and Steller’s jays. Ecology 85:1411-1427.

Millspaugh, J.J., R.M. Nielsen, L. McDonald, J.M. Marzluff, R.A. Gitzen, C.D. Rittenhouse, M.W. Hubbard, and S.L. Sheriff. 2006. Analysis of resource selection using utilization distributions. Journal of Wildlife Management 70:384-395

Royle, J.A., and J.D. Nichols. 2003. Estimating abundance from repeated presence-absence data of point counts. Ecology 84:777-790