FW662 — Wildlife Population Dynamics

Content: Characteristics of changes; scientific basis for analysis and modeling of wildlife populations.

Philosophy: Analysis of populations is a quantitative discipline. Mathematical models are required to understand and predict population behavior. Therefore, this course will be structured upon a foundation from mathematical models, expanding to the biological evidence to support and/or reject various factors regulating populations. To conclude the course, population management strategies will be introduced.

Instructors: Gary C. White, Dana L. Winkelman.

Teaching Assistant:

Objective: Students will develop competence 1) to build realistic population models and run them on a PC; 2) to assess the rate of increase/decrease of a population given birth, death, immigration, and emigration rates; and 3) to have a familiarity with both the biological and mathematical literature of population dynamics.

Credit: 3 hr

Pre-requisites: Calculus, FW360, computer literacy (especially Excel spreadsheets).

Schedule: Classes will meet twice as often during the first half of the semester, so that students needing to leave campus for field work can do so. Formal lectures will be given on Mondays and Wednesdays, and computer laboratory exercises and discussion groups will meet on Fridays.

Class Times:    MW 8-11 Lectures and Recitations, F 8-10 Computer Labs.

Discussion Sessions: Students will be expected to have read the material pertaining to lectures during the week, plus reviewed their class notes, and be ready to discuss the material presented. In addition, students are encouraged to bring in literature from their area of expertise for discussion by the class, and the further schooling of the instructors.

Project:  Each student will develop a population model for a species of their interest, and present the model to the class during the latter part of the class.  We envision a 20-minute presentation.

Grading: A midterm (take-home, written exam) and final (25 minute oral exam) exams will be given. Final grades will be based on these exams.  Participation in class, particularly thoughtful comments in discussion groups and the insights of their model presented to the class, will help decide borderline grades, i.e., instructor discretion.

Policy on Cheating: Any student found cheating in this course will be given an F for the course, and the Graduate Affairs Committee of the Department of Fishery and Wildlife will be petitioned to expel the student from the major.

Important Dates:

Midterm — Feb. 11, due Feb. 14, 8:00am.

Final Exam — Mar. 21-22, oral exam, 25 minutes each.

This page has been accessed times since August 16, 2000.

Last Modified January 18, 2005

Schedule of Lectures and Labs

Jan. 19 — Lecture 1. Introduction, Density-independent population growth models; differential vs. difference equations.

Reading:
Gotelli, 2001, A Primer of Ecology, Chapter 1, pages 2-23.
Jan. 21 — Discussion and Laboratory 1.
Objective: Become familiar with the use of Computer Learning Laboratory, and comfortable with use of Excel or Quattro Pro spreadsheet program.
Jan. 24 — Lecture 2. Density-dependent population growth models: logistic, Ricker, and Schaefer models; maximum sustained yield.

Reading:
Gotelli, 2001, A Primer of Ecology, Chapter 2, pages 25-48.
Jan. 26 — Lecture 3. Effects of time delays, overcompensation, chaos, on logistic-type models.

Reading:
Renshaw (1991) Chapter 4, Time-lag models of population growth, pages 87-127, and
Optional:
Ritchie, M. E. 1992. Chaotic dynamics in food-limited populations: implications for wildlife management. Pages 139-147 in D. R. McCullough and R. H. Barrett, eds. Wildlife 2001: Populations. Elsevier Applied Science, New York, N. Y.
Jan. 28 — Discussion and Laboratory 2.
Objective: Become familiar with the density-dependent models.
Jan. 31 — Lecture 4. Population Models Incorporating Stochasticity.

Reading:
Renshaw (1991) Chapters 2 Simple birth-death processes, and 3 General birth-death processes, Pages 15-86.
Optional:
Lebreton, J.-D. 1990. Modeling density dependence, environmental variability, and demographic stochasticity from population counts: an example using Wytham Wood Great Tits. Pages 89-102 in Population Biology of Passerine Birds, J. Blondel (ed.). Springer-Verlag Berlin Heidelberg.
Feb. 2 — Lecture 5. Age- and stage-structured models: Leslie-Lefkovitch Models.

Reading:
Gotelli, 2001, A Primer of Ecology, Chapter 3, pages 49-80.

Noon, B. R. and J. R. Sauer. 1992. Population models for passerine birds: structure, parameterization, and analysis. Pages 441-464 in D. R. McCullough and R. H. Barrett, eds. Wildlife 2001: Populations. Elsevier Applied Science, New York, N. Y.
Lande, R. 1991. Population dynamics and extinction in heterogeneous environments: the Northern Spotted Owl. Pages 566-580 in C. M. Perrins, J-D. Lebreton, and G. J. M. Hirons, eds. Bird Population Studies, Oxford, New York, N.Y.
Feb. 4 — Discussion and Laboratory 3.
Objective: Explore discrete stochastic models, and age-specific models.
Feb. 7 — Lecture 6. Mechanisms and evidence for density dependence. Senescence.

Reading:
Sinclair, A. R. E. 1989. Population regulation in animals. Pages 197-241 in J. M. Cherrett, ed. Ecological concepts. Blackwell scientific Publ., Oxford, U.K.
Optional:
Ricklefs, R. E. 1990. Ecology, Chapter 19, Evolution, Social Behavior, and Population Regulation, Pages 367-382. W. H. Freeman, New York, N.Y.
Feb. 9 — Lecture 7. Additive vs. compensatory mortality and MSY.

Reading:
Nichols, J. D., M. J. Conroy, D. R. Anderson, and K. P. Burnham. 1984. Compensatory Mortality in waterfowl populations: a review of the evidence and implications for research and management. Transactions North American Wildlife and Natural Resources Conference 49:535-554.
Optional:
Nichols, J. D. 1991. Responses of North American duck populations to exploitation. Pages 498-525 in C. M. Perrins, J-D. Lebreton, and G. J. M. Hirons, eds. Bird Population Studies, Oxford, New York, N.Y.

Smith, G. and R. Reynolds. 1992. Hunting and mallard survival. Journal of Wildlife Management 56:306-316.

Sedinger, J. S., and E. A. Rexstad. 1994. Do restrictive harvest regulations result in higher survival rates in mallards? A comment. Journal of Wildlife Management 58:571-577.

Smith, G. and R. Reynolds. 1994. Hunting and mallard survival: a reply.  Journal of Wildlife Management 58:578-581.

Clark, W. R. 1987. Effect of harvest on annual survival of muskrats. Journal of Wildlife Management 51:265-272.
Feb. 11 — Discussion and Laboratory 4. Midterm Exam Handed Out.
Objective: Understand how compensation in survival can result in overall higher survival, and discuss previous midterm exams.
Feb. 14 — Lecture 8. Spatially-structured populations.

Reading:
Gotelli, 2001, A Primer of Ecology, Chapter 4, pages 81-97.

Hanski, I. 1996. Metapopulation ecology. Pages 13-43 in Rhodes, O. E., Jr., R. K. Chesser, and M. H. Smith (eds.). Population dynamics in ecological space and time. Univ. Chicago Press, Chicago, Ill.

Pulliam, H. R. 1996. Sources and sinks: empirical evidence and population consequences. Pages 45-69 in Rhodes, O. E., Jr., R. K. Chesser, and M. H. Smith (eds.). Population dynamics in ecological space and time. Univ. Chicago Press, Chicago, Ill.
Optional:
Gilpin, M. E. 1987. Spatial structure and population vulnerability. Pages 125-139 in M. E. Soul�, ed. Viable Populations for Conservation. Cambridge Univ. Press, New York, N.Y. 189 pp.

Kareiva, P. 1990. Population dynamics in spatially complex environments: theory and data. Pages 53-68 in Population Regulation and Dynamics, M. P. Hassell and R. M. May, eds. The Royal Society, London.
Feb. 16 — Lecture 9. Role of immigration and emigration in populations.

Reading:
Sinclair, A. R. E. 1992. Do large mammals disperse like small mammals? Pages 229-242 in Stenseth, N. C., and W. Z. Lidicker, Jr. Eds. Animal dispersal small mammals as a model. Chapman and Hall, New York, N.Y. 365 pp.
Optional:
Greenwood, P. J. 1983. Chapter 7. Mating systems and the evolutionary consequences of dispersal. Pages 116-131 in I. R. Swingland and P. J. Greenwood, eds. The ecology of animal movement. Clarendon Press, Oxford, England.

Stenseth, N. C. 1983. Chapter 5. Causes and consequences of dispersal in small mammals. Pages 63-101 in I. R. Swingland and P. J. Greenwood, eds. The ecology of animal movement. Clarendon Press, Oxford, England.
Feb. 18 — Discussion and Laboratory 5.
Objective: Explore the role of spatial variation in simple population models.
Feb. 21 — Lecture 10. Predation, Parasitism, and Herbivory.

Reading:
Gotelli, 2001, A Primer of Ecology, Chapter 6, pages 125-153.

Renshaw (1991) Chapter 6 Predator-prey processes, Pages 166-204.
Optional:
Gasaway, W. C., R. D. Boertje, D. V. Grangaard, D. G. Kelleyhouse, R. O. Stephenson, and D. G. Larsen. 1992. The role of predation in limiting moose at low densities in Alaska and Yukon and implications for conservation. Wildlife Monograph 120. 59 pp.

Boutin, S. 1992. Predation and moose population dynamics: a critique. Journal of Wildlife Management 56:116-127.
Krebs, C. J. et al. 1992. What drives the snowshoe hare cycle in Canada’s Yukon? Pages 886-896 in D. R. McCullough and R. H. Barrett, eds. Wildlife 2001: Populations. Elsevier Applied Science, New York, N. Y.
Feb. 23 — Lecture 11. Competition.

Reading:
Gotelli, 2001, A Primer of Ecology, Chapter 5, pages 99-124.

Renshaw (1991) Chapter 5 Competition processes, Pages 128-165.
Optional:
Schoener, T. W. 1983. Field experiments on interspecific competition. Amer. Nat. 122:240-285.
Feb. 25 — Discussion and Laboratory 6.
Objective: Explore the role of species interactions in population models, also incorporating stochasticity into these models.
Feb. 28 — Lecture 12. Natural Selection and Population Regulation.

Reading:
Shields, W. M. Chapter 8. Optimal inbreeding and the evolution of philopatry. Pages 132-159 in I. R. Swingland and P. J. Greenwood, eds. The ecology of animal movement. Clarendon Press, Oxford, England.

Meffe, G. K. 1986. Conservation genetics and the management of endangered fishes. Fisheries 11(1):14-23.
Optional:
Hedrick, P. W. and P. S. Miller. 1992. Conservation genetics: techniques and fundamentals. Ecological Applications 2:30-46.

Lande, R. and G. Barrowclough. 1987. Effective population size, genetic variation, and their use in population management. Pages 87-123 in M. E. Soul�, ed. Viable populations for conservation. Cambridge Univ. Press, New York, N.Y.

Jim�nez, J. A., K. A. Hughes, G. Alaks, L. Graham, and R. C. Lacy. 1994. An experimental study of inbreeding depression in a natural habitat. Science 266:271-273.
Mar. 2 — Lecture 13. Management of populations.

Reading:
Hilborn, R. and C. J. Walters. Chapter 3. Behavior of exploited populations. Pages 47-103 in Quantitative fisheries stock assessment. Chapman and Hall, New York, N.Y. 570 pp.

Ludwig, D., R. Hilborn, and C. Walters. 1993. Uncertainty, resource exploitation, and conservation: lessons from history. Science 260:17,36.

Stacey, P. B. and M. Taper. 1992. Environmental variation and the persistence of small populations. Ecological Applications 2:18-29.
Optional:
Anderson, D. R. 1985. Constrained optimal exploitation: a quantitative theory. Pages 105-116 in S. L. Beasom and S. F. Roberson, Game Harvest Management.

McCullough, D. R. 1984. Lessons from the George Reserve, Michigan. Pages 211-242 in White-tailed deer ecology and management. Wildlife Management Institute.

Walter, C. 1986. Chapter 4. Models of Renewable Resource Systems. Pages 64-128 in Adaptive Management of Renewable Resources. Macmillian, New York, N.Y. 374 pp.
Mar. 4 — Discussion and Laboratory 7.
Objective: Explore simple genetics models; and examine a stochastic model based on data.
Mar. 7 — Lecture 14. Estimating Variance Components.

Reading:
Burnham, K. P., D. R. Anderson, G. C. White, C. Brownie, and K. H. Pollock. 1987. Design and Analysis Experiments for Fish Survival Experiments Based on Capture-Recapture. Am. Fish. Monograph No. 5, Pages 260-278.
Mar. 9 — Lecture 15. Minimum Viable Population Models, Estimating Population Persistence Probabilities, Review.

Reading:

Boyce, M. S. 1992. Population viability analysis. Annual Review of Ecology and Systematics 23:481-506.

Optional:
Foley, P. 1994. Predicting extinction times from environmental stochasticity and carrying capacity. Conservation Biology 8:124-136.

Berger, J. 1990. Persistence of different-sized populations: an empirical assessment of rapid extinctions in bighorn sheep. Conservation Biology 4:91-98.

Tracy, C. R., and T. L. George. 1992. On the determinants of extinction. American Naturalist 139:102-122.

Tracy, C. R., and T. L. George. 1993. Extinction probabilities for British island birds: a reply. American Naturalist 142:1036-1037.
Mar. 11 — Discussion and Laboratory 8.
Objective: Estimate process variance from a series of observations with sampling errors; explore simple population viability models, particularly the affect of individual heterogeneity.
Mar. 21-22.  Oral Final Exam.

Last Modified October 27, 2004

Primary References

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Emlen, J. M. 1984. Population biology — the coevolution of population dynamics and behavior. Macmillan, New York, N. Y. 547 pp.

Renshaw, E. 1991. Modelling biological populations in space and time. Cambridge Univ. Press, New York, N. Y. 350 pp.

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