Course Web Site: http://www.ac.wwu.edu/~jmcl/Popbiol/syl_2009.htm

Text: (recommended) Gotelli, Nicholas J. 2001. A Primer of Ecology, 3^rd ed.
Additional readings as assigned: available in course binder in Huxley student resource center, ES 545

Prerequisites: Ecology (ESCI 325 or BIOL 325),
Calculus (MATH 124; MATH 125 recommended),
Biostatistical Analysis (ESCI 340 or BIOL 340)

Course Description:

This course provides an examination of the structure, distribution, and dynamics of populations. Both empirical and theoretical approaches will be emphasized. Population biology is inherently quantitative; students will be expected to use mathematics to explain population concepts and to analyze population data. On completion of the course, students should be able to apply information about populations to (1) describe how various factors influence their dynamics, (2) determine effects of age structure and spatial structure on population growth and persistence, (3) explain how interactions with other species affect a population's distribution and abundance, and (4) predict the effectiveness of programs for population conservation and management.

Course Evaluation:

Grades will be based on five bi-weekly problem sets and a comprehensive take-home final examination.
Problem sets are due in class at noon on the date indicated.
Problem sets will comprise 75% of the course grade. The final exam will contribute the remaining 25%.
To enhance learning derived from problem sets, each may be resubmitted once for re-grading.
The re-submission deadline will be stated on the problem set, usually two weeks after the original due date.
Given the opportunity for resubmission, problem sets submitted or resubmitted late will not be accepted.
If a problem set is not resubmitted, the original score will stand.
If a problem set is resubmitted, the original score score will count 10% toward its final grade,
and the regrade will contribute the remaining 90%. (This policy is to support resubmission as an opportunity
for feedback and learning, rather than simply a means to extend the submission deadline.)

Course Schedule:
 

Week	Topics	Reading
Jan. 7 7 pm	Grizzly Wars: The Public Fight Over the Great Bear David Knibb: author event (not a course event; attendance optional)	Village Books, 1200 Eleventh St., Bellingham
Jan. 9	Predators that learn Individuals and groups	Meet in AH 12
Jan. 12	Refine study systems from student interest survey
Jan. 14	Invasive species ESA alert: Nonnative Wildlife Invasion Prevention Act	Pimentel et al. 2000 Lodge et al. 2006
Jan. 16	Simulating spread of invasive species Example for Spartina alterniflora, in R Function to iterate invasive spread model Function to iterate area occupied Simulating STOCHASTIC spread of invasive species Example for Spartina alterniflora, in R Function to iterate invasive stochastic spread model Function to iterate area occupied w/ stochastic spread	Meet in AH 5. Arim et al. 2006
Jan. 21 4 pm	Comparing trophic ecology of aquatic and terrestrial ecosystems Jonathan Shurin, Assoc. Prof., Dept. Zoology, Univ. British Columbia Biology Department Seminar, BI 234 (not a course event; attendance optional)
Jan. 24 (Sat.)	Field trip: Populations in winter Attendance optional Avalanche forecast	Meet 8:30am ES garage doors
Feb. 2	Population Viability Analysis Population Reintroductions
Feb. 9	Marine protected areas and fisheries management Metapopulations example: 3-patch metapopulation matrix calculations	Gotelli, ch. 4 Hanski 2003 Ludwig, Hilborn, & Walters 1993 Hastings & Botsford 1999 Malakoff, D. 2001; Pauly, et al. 2003 Krkosek, et al. 2007 Byers & Noonburg 2007
Feb. 16	President's Day -- No class
Feb. 18	Predator-prey interactions Trophic Structure and Dynamics	Gotelli, ch. 6 Krebs et al. 1995; Hanski et al. 1993 McLaughlin & Roughgarden 1993 Ruzycki et al. 2003 Rockwood ch. 9
Feb. 23	Mutualistic interactions	TBA
Feb. 27 3pm	David Knibb, "Saving Endangered Species: What Grizzly Bears Have Taught Us" Huxley speaker series (attendance optional)	CF 420

Course content and schedule will depend in part on student interests.
See link below for a partial list of potential topics.
http://www.ac.wwu.edu/~jmcl/Popbiol/example2.pdf

Extra-curricular Events: Human Population Topics
 

Date	Event	Location
Jan. 26 3 pm	David Bacon: "Illegal People: How Globalization Creates Migration and Criminalizes Immigrants" (not a course event; attendance optional)	CF 110
Jan. 31 10am-noon	Citizens' Forum: Why should we care about population planning? Open discussion on Whatcom 2031 population projections (not a course event; attendance optional)	Fountain Community Church 2100 Broadway, Bellingham
Feb. 12 7pm	Public Hearing: Review the 20-year population and employment growth forecasts for Bellingham and Whatcom County Bellingham Planning Commission (not a course event; attendance optional)	City Council Chambers 210 Lottie St., Bellingham
Feb. 17 7pm	Public Hearing: EIS scope, Urban Growth Area 10-year review Joint hearing: Whatcom County Council, Whatcom County Planning Commission (not a course event; attendance optional)	Whatcom County Council Chambers 311 Grand Ave., Bellingham
Mar. 4 3pm	Katey Walter: Carbon cycling, greenhouse gas emissions from aquatic ecosystems, and climate change. Huxley speaker series.	CF 420

Population Concepts:
 

Topics	Reading
Population growth Population limitation Application: fisheries management Example: Ricker model simulation, w/, w/out sea lice, in R Function to iterate Ricker model	Gotelli, ch. 2, appendix: pp.203-214 Ludwig, Hilborn, & Walters 1993 Hastings & Botsford 1999 Malakoff, D. 2001; Pauly, et al. 2003 Krkosek, et al. 2007 Byers & Noonburg 2007
Simulating stochastic populations Example of logistic simulations, in R Example of stochastic logistic simulations, in R Function to iterate logistic model Function to iterate stochastic logistic model Function, stoch. logistic w/out on-screen output
Age-structured populations	Gotelli, ch. 3
Numerical methods for age-structured populations Leslie matrix calculations, lambda and c(x) Leslie matrix calculations for GYE grizzly bears, nonmast years Leslie matrix calculations for GYE grizzly bears, mast years Example: simulating grizzly population growth in mast years Function to iterate Leslie matrix for age-structured population Example: simulating grizzly population growth in variable environment
Population dynamics, part I	Murdoch 1994; Turchin 1995 Bjornstat & Grenfell 2001 Dixon, Milicich, & Sugihara 1999
Population dynamics, part II Biology of human populations	Hastings et al. 1993 Zimmer 1999 Vandermeer & Goldberg 2003, ch. 4 McLaughlin et al. 2002a,b Cohen 1995, 2003; Jenkins 2003 WA OFM 2002
Simulating complex dynamics
Simulating complex dynamics Population Viability Analysis (PVA) Example: population simulations with variable lambda, in R Function to iterate growth model with variable lambda Function to iterate growth model, w/out on-screen output Function to iterate growth model, extinction -> zero Example: simplified population viability analysis Function to repeat model simulations for PVA Example: PVA for an age-structured population
Spatially structured populations example: 3-patch metapopulation matrix calculations	Gotelli, ch. 4 Tilman, Lehman, & Karieva 1997 McLaughlin & Landis 2000 Hanski 2003
Predator-prey interactions Example: simulating dynamics generated by 3D trophic interactions Example: simulating dynamics generated by 3D trophic interactions, with exogenous noise Disease dynamics	Gotelli, ch. 6 Krebs et al. 1995; Hanski et al. 1993 McLaughlin & Roughgarden 1993 Ruzycki et al. 2003 Rockwood ch. 9
Numerical methods for spatially-structured populations
Competitive interactions	Gotelli, ch. 5 Roughgarden et al. 1988
Numerical models for disease epidemics
Community structure and dynamics	Gotelli, ch. 8 Fauth et al. 1996 McLaren & Peterson 1994 Estes et al. 1998 Jones et al. 1998 Smith et al. 2003
Individual-based Lotka-Volterra predator-prey model in space (torous); Developed by Lawrence Leinweber Function to iterate discrete-time Lotka-Volterra predator-prey model

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Problem Sets
Problem sets are due at noon on the date listed.

Reading: Readings may be revised according to student interests.