Site Index

Site Index

• Course syllabus
• Course Text
• How to succeed in the course
• Lecture
• Laboratory
• Course Listserve
• Formatting data for capture-recapture
• Course Project
• Exams
• Using Python to Code Data Input
• Diversions
• Site Management

LECTURE AND READINGS: The lectures will generally follow the Williams et al. text. However, I will assume that you have read the assigned text material in detail, and the lectures are intended to complement, rather than repeat, this material. In addition, additional ideas, not necessarily, covered in the text, may be introduced in lectures or labs. You are responsible for the information in the assigned readings, regardless of whether it is covered in lecture, and vice versa.

As can be seen from a scan of the course syllabus, and text table of contents, there are four broad themes or "parts" to the course": (1) introductory, contextual material on modeling, logic, and statistics, (2) dynamic modeling of populations, (3) estimation methods, and (4) applications to decision making. Time-wise, most of the emphasis in the course is on (3), but all four parts are inextricably linked and are essential. The desired effect is that each part will build on the previous parts. One implication of this is that much of the early material will be "recycled" continually through the course. If a topic doesn't appear especially relevant right away, chances are very good that it will later on. Your best "study device" is to look for, and find, the connections between general principles (tending to be taught early on) and specific applications (tending to come later). I will try to help you see some of these, but a full appreciation of the material will come only from your active engagement in this process.

LABORATORIES : These are intended to give practical, hands on experience in the quantitative methods taught in the course. Early labs will emphasize the development of some basic modeling and statistical skills, and will require a certain amount of simple programming. However, most of these will be spreadsheet Python programs that are easy to learn, and require no previous exposure to computer programming. Later applications will rely on a combination of spreadsheet programs (developed by the instructor), Python, SAS, or R programs, or MS-DOS and WINDOWS programs; in most of these the user is not actively engaged in programming. Regular lab assignments will be made and will ordinarily be due at the beginning of the next lab period. Only under the most extraordinary circumstances will I accept reports late; if you can't be in lab when the assignment is due, it is up to you to get it done and turned in ahead of time.

PROJECT: A required project will constitute a major portion of the grade. The project will require synthesis of all of the course elements, and application to solving a real or hypothetical management problem. See Project.

MATHEMATICS: Occasionally in the past there have been misunderstandings about the content and direction of this course, and my expectations of students taking the course. The preceding should help clarify these points, but sometimes I hear from students that the course is "too mathematical" or "should be more oriented toward field sampling." Although this course is ultimately motivated by, and directed toward, applications of up-to-date mathematical and statistical tools to population analysis, it is mathematical. In order to understand the proper application and context of these methods, some theory is essential, and that theory necessarily involves mathematics: logic, algebra, calculus, systems of equations, and elementary probability theory. I make no apologies for these requirements. If you are seeking a course that is field oriented, "cookbook" methods oriented, descriptive, or otherwise non mathematical (and non rigorous), you are in the wrong course, and I advise you to immediately drop the course and take one more suited to your needs and interests.

EFFORT AND DIFFICULTY : The course will involve much reading and synthesis of information from lectures, labs, reading, projects, and class discussions. I provide as much direction as I can but students are expected to be self-motivated and to put forth additional effort to integrate material. Thus, you can expect to put in more effort in this class than for the typical 4-hour class. I think if you do, you will find it worth your effort in your careers. If you are not willing or able to put in this effort, this class may not be for you.

EXAMS : Two exams, a midterm and a final, will be given in the course. Both will be cumulative to the time of the exam, and will emphasize synthesis and application of the course material, as opposed to rote memory. The exams will be given in class, during a regular lecture period, and will be open book.

ORGANIZATION: Please recognize that the course is dynamic; the field is continually changing, requiring the continual introduction of new material, and the reorganization of topics. Occasionally this might result in a bit of an "under construction" feel. For instance, this semester I am going to try to introduce Bayesian approaches alongside the traditional likelihood-based approaches emphasized in the text. This may be confusing and require some outside reading for full understanding, but I feel that introducing these approaches is very important in the field (and they were just getting going when we wrote the textbook). I hope you will see this is an acceptable tradeoff, in that what you are being taught really is up to date, in a field where textbooks typically are out of date in five years (witness the two supplementary "updates" (1986 and 1992)to Seber's 1982 2^nd edition "Estimation of Abundance." As suggested above, some aspects of the Williams et al. text, as well as these earlier references, are already out of date!

HELP: If something doesn't make sense, please ask: chances are someone else was wondering too. Occasionally, it doesn't make sense because it's wrong. Mistakes are, in fact, a great way to learn. If you need additional help understanding a topic, or want to discuss class material in more detail, please either see me during office hours, or if these aren't convenient, make an appointment, and I will try to meet with you within the next day or so.

FEEDBACK: I always appreciate constructive feedback as to how the course could be better taught. You are welcome to provide these to me personally (e.g., during office hours), in writing anonymously, as part of the (anonymous) course evaluation process, or all of the above.