WILD 7930
HABITAT SELECTION, USE, AND OCCUPANCY
I. 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 – steury@auburn.edu
Web-site: http://www.auburn.edu/~tds0009
II. Pre-requisite
STAT 7000 or similar statistics course that covers single factor ANOVA and
regression
III. Course Format
The course will consist of two 2-hour lectures followed by two 2-hour labs per 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.
IV. 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.
V. 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.
VI. Course Objectives
1. Familiarize the student with the 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).
2. Familiarize the student with the advantages and disadvantages of different data
types, as well as pitfalls associated with interpretation.
3. Train the student how to perform habitat analysis using various software such as
MYSTAT, MS-Excel, the computing language R, and the package PRESENCE.
VII.
Course Requirements/Evaluation
Students will be evaluated via assignments, quizzes, and a final exam. Assignments
will include weekly reports detailing 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; 7080% = C; 60-70% = D; <60% = F).
VIII.
Course Policies
Students are expected to attend all lectures and labs.
IX. 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.
X. 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 7000 or 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.
XI. 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: scw0005@auburn.edu.
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 85:14111427.
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