DistanceSampling.pptJanuary 24, 2007

FW663 — Sampling & Analysis of Vertebrate Populations

bullet  Gary C. White
bullet      Paul F. Doherty, Jr.
bullet       TA

Study Sessions:

To be determined

FW663 is designed to include a balance of science philosophy, statistical theory and biological application. While the overall theme of the course deals with sampling and analysis theory for biological populations, the course is more broad, providing the advanced student with the following:

  1. Some philosophy of inductive inference (e.g., estimation of parameters and measures of precision).
  2. A critical attitude concerning “results” and “findings” and an appreciation of the importance of underlying assumptions.
  3. State-of-the-science information on sampling, analysis, and inference theory for populations in terrestrial and aquatic environments.
  4. Practical experience in sample design, data collection, analysis and inference in several experimental situations.
  5. Current publications and bibliographies useful for future reference.
  6. Familiarity with computer software for the sophisticated exploration of complex biological problems.

The first lectures will cover background material (to bring all students up to the same level) and general methodologies (inference methods, strong inference, and statistical concepts such as mean square errors, power of tests, likelihood functions, profile likelihoods, model selection philosophy, Akaike’s Information Criterion, components of variance, properties of estimators and information matrices).

The main section of the course will deal with estimation of population size, survival rates (finite and instantaneous), and birth rates from various types of sampling data from animal populations. The course will cover the following topics:

Sample data consisting of both marked and unmarked animals.

Capture-recapture models: Both open and closed population models. This topic covers a large class of methods allowing birth and death rates and/or population size to be estimated.

Sample data consisting of only marked animals.

Survival estimation from tagged, banded, and radioed animals: Age and/or time-specific survival rates, constant or variable effort.

Sample data consisting of only unmarked animals.

Occupancy estimation: detection probabilities <1.

Removal models: Constant and variable effort, closed populations.
Catch-curve models: Constant effort, stable and stationary populations, data are recorded by age or size class.
Generalized distance sampling: Line and point transect population sampling.
Change in ratio models: Constant effort, closed populations, sampling before and after a class-specific population change.

The above methods often have comprehensive software available (NOREMARK  plus User’s Manual; MARK including the Euring 97 paper published in 1999, Evan Cooch’s Gentle Introduction (continually updated), and papers from the 2nd IWMC in Hungary; DISTANCE plus User’s Guide, RELEASE plus the User’s Manual; and CAPTURE plus the User’s Manual) and laboratory sessions will make extensive use of these packages in the Warner College of Natural Resources  PC Computer Laboratory.

Major source materials will include (electronic copies of some of these available here):

bullet Brownie, C., D. R. Anderson, K. P. Burnham, and D. S. Robson. 1985. Statistical inference from band recovery data — a handbook, 2nd ed. U. S. Fish and Wildlife Service Research. Publication 156, Washington, D. C. 305pp.
bullet Buckland, S.T., D.R. Anderson, K.P. Burnham, and J.L. Laake. 1993. Distance sampling: estimating abundance of biological populations. Chapman and Hall, New York, N.Y. 446 pp.
bullet Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. J. Thomas.  2001.  An introduction do distance sampling: estimating abundance of biological populations.  Oxford University Press, Oxford, UK.  432pp.
bullet Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, L. J. Thomas (Editors). 2004. Advanced Distance Sampling. Oxford University Press, Oxford, UK.
bullet Burnham, K. P., J. L. Laake, and D. R. Anderson. 1980. Estimation of density from line transect sampling of biological populations. Wildlife Monograph 72:1-202.
bullet Burnham, K. P. 1993. A theory for combined analysis of ring recovery and recapture data. Pages 199-213 in J.-D. Lebreton and P. M. North, eds. Marked Individuals in the Study of Bird Population. Birkhauser Verlag, Basel, Switzerland.
bullet Burnham, K. P., D. R. Anderson, G. C. White, C. Brownie, and K. H. Pollock. 1987. Design and analysis methods for fish survival experiments based on release-recapture. American Fisheries Society Monograph 5:1-437.
bullet Burnham, K. P., and D. R. Anderson.  2002.  Model selection and multimodel inference: a practical information-theoretic approach.  2nd edition.  Springer-Verlag, New York, New York, USA.  488pp.
bullet Cooch, Evan, and G. C. White.  1999.  Program MARK: First Steps.
bullet Lebreton, J.-D., K.P. Burnham, J. Clobert, and D.R. Anderson. 1992. Modeling survival and testing biological hypotheses using marked animals: case studies and recent advances. Ecological Monograph 62:67-118.
bullet Euring 2003.  2004.  Entire volume of Animal Biodiversity and Conservation 27.1:1-572.
bullet Otis, D. L., K. P. Burnham, G. C. White, and D. R. Anderson. 1978. Statistical inference from capture data on closed animal populations. Wildlife Monograph 62:1-135.
bullet Pollock, K.H., J.D. Nichols, C. Brownie, and J.E. Hines. 1990. Statistical inference for capture-recapture experiments. Wildlife Monograph 107:1-97.
bullet Rexstad, E. A., and K. P. Burnham.  1991.  User’s manual for interactive Program CAPTURE.  Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, Fort Collins, CO.  29 pp.
bullet Seber, G. A. F. 1982. The estimation of animal abundance and related parameters, 2nd ed. Macmillan, New York, NY.
bullet Thomas, L., S. T. Buckland, K. P. Burnham, D. R. Anderson, J. L. Laake, D. L. Borchers, and S. Strindberg. 2002. Distance sampling. Pp. 544-552, Volume 1, in Encyclopedia of Environmetrics, El-Shaarawi, A. H. and W. W. Piegorsh (Eds). John Wiley and Sons Ltd. Chichester.
bullet Thompson, W. L., G. C. White, and C. Gowan.  1998.  Monitoring vertebrate populations.  Academic Press, San Diego, California, USA.  365pp.
bullet Thompson, W. L. (ed.).  2004.  Sampling Rare or Elusive Species.  Island Press, Washington, D.C., USA.  429pp.
bullet White, G. C., D. R. Anderson, K. P. Burnham, and D. L. Otis. 1982. Capture-recapture and removal methods for sampling closed populations. Los Alamos National Laboratory Report LA-8787-NERP, Los Alamos, NM. 235pp.

Grades will be assigned based on the following weights:

bullet 20% Quizzes (average with lowest 2 quiz scores discarded)
bullet 30% Midterm I
bullet 20% Midterm II
bullet 30% Final Exam

Class participation will be used to help decide borderline grades, i.e., a person that falls on the A/B line may receive an A if class participation was high. This person would have asked questions in class, and helped keep the discussion sections interesting. In contrast, a lack of participation might mean a B for a person on the borderline between an A and a B.

Laboratory exercises will not be graded, but examined to see how well you understood the questions and the objective of the lab. You are encouraged to work together in the laboratory. Particularly important is that students with prior computer knowledge help students without prior computer training. Not understanding a laboratory exercise means you have probably missed an important concept. Lab performance is measured through the midterms and the final exam. You will likely be asked to perform an analysis in a take-home exam similar to a laboratory exercise.

Some Details:

FW663 is intensive, partially because it is offered on an accelerated basis.  Each 4 hour period is 1.5 hrs. lecture, 0.5 hrs. recitation and 2 hrs. of laboratory.

Students would be unwise to take more than one other course concurrent with FW663. A graduate seminar or ST512 are good choices. FW663 will take every available hour for most graduate students; do not get over-committed during Spring Semester.

“Homework” is usually estimated as # credits times 2 hrs./week.  FW663 is taught at 1.5 pace, thus 5 x 2 x 1.5 = 15 hours per week should be scheduled for “homework and study” for this class.  Students that are statistically challenged or computer impaired should allow additional time.  With 12 hours of class time and at least 15 hours of “homework” it becomes clear that FW663 will occupy a solid 30 hours per week of a student’s time.

Expect an almost daily quiz; this keeps everyone learning and allows the instructors to gauge understanding and reinforce certain points where understanding is low. The daily quiz is an important learning tool. In addition, quizzes force everyone to “keep up.”

Laboratory sessions try to facilitate team-work. Get used to working together to solve problems and gain understanding.

ALWAYS use ONLY your student ID (not your name) on all material to be turned in (e.g., Social Security Number on quizzes, mid-term and final examinations).  Using only your social security number aids in objective grading of the material.

Meet Date Day


1 1/24 Wed Statistics Review, Binomial and Multinomial Coefficients, Random Samples, Expected Value of an Estimator Orientation of CNR Computer Lab
2 1/26 Fri Binomial Sampling and Binomial Distribution, Binomial Likelihood Function, Maximum Likelihood Estimation, Likelihood Ratio Tests,Reading Binomial sampling, SAS Input
3 1/29 Mon Multinomial Distribution, Confidence Intervals, Future Course Directions, MLE’s not in Closed Form, Reading ML exercise with binomial distribution, SAS Input
4 1/31 Wed Band Recovery Models, Parameter Index Matrix (PIM), Program MARK, Reading Brook trout, Input data, MARK DBF, MARK FPT
5 2/02 Fri Survival estimation, band recoveries, AIC , AIC Lecture Notes, AIC Handout, Reading Sage grouse, Input data, MARK DBF, MARK FPT
6 2/05 Mon Covariates in survival estimation, design matrix , PIMs to Design MatrixReading Grouse data with design matrix, MARK DBF, MARK FPT
Wed More on design matrices and model selection , Slide Show, Color Slide Show, Reading Class exercise — design matrices Work with MARK to understand design matrices
8 2/09 Fri Cormack-Jolly-Seber models , Reading European dipper data, Input data, MARK DBF, MARK FPT
9 2/12 Mon Quasi-likelihood, c-hat, extra-binomial variation , Reading Starling/DDT dosage data,Input data, MARK DBF, MARK FPT
10 2/14< Wed

Joint analysis of live & dead encs., Reading, Barker’s model, Pradel model

Goldeneyes joint live and dead encounters, pikeminnow Pradel model, Barker input data, Barker DBF, Barker FPT, Pradel input data, Pradel DBF, Pradel FPT
11 2/16 Fri Monte Carlo simulations, Reading, Design and analysis of survival estimation experiments Design of starling/DDT dosing, RELEASE Input, Example Simulation Input and Output
12 2/19 Mon

Review, Questions, Hand out exam, Files: terns.inp, Mallards.inp,NSO.inp

13 2/21 Wed *** Midterm I *** Take-home, Due Wednesday 2/21, 4:00pm
14 2/23 Fri Survival estimation with radioed animals   (r = p = 1), logistic regression, individual covariates, Reading Program MARK Mule deer fawn survival, Input data, MARK DBF, MARK FPT, and SAS logistic regression with body condition SAS Input, SAS Data.
15 2/26 Mon Capture-recapture closed models, likelihoods, closure, individual heterogeneity, Reading Programs MARK and CAPTURE with capture-recapture data, SAS Simulator, Input data,MARK DBF, MARK FPT
16 2/28 Wed Removal estimation methods, model averaging, profile likelihoods,Reading Program MARK with house mouse and electrofishing data, coulombe.inp, coulombe.dbf,coulombe.fpt, removal.inp,removal.dbf, removal.fpt,Huggins Removal.inp, Huggins Removal.dbf, Huggins Removal.fpt
17 3/02 Fri Robust design, Multi-strata design,Reading Program MARK Robust design data, robust.inp, robust.dbf, robust.fpt, mssurv.inp, mssurv.dbf, mssurv.fpt
18 2/05 Mon Variance Components, 2nd VC Write-up, Hypothesis Testing,Reading Review, questions, additional work with robust design and multi-strata model,BurnhamExample.inp,BurnhamExample.DBF,BurnhamExample.FPT,Mallards.inp, Mallards.dbf,Mallards.fpt
19 3/07 Wed Model conceptualization exercise,Reading, Review, questions Review, questions, Exam handed out
20< 3/09 Fri *** Midterm II *** Take-home SeaTurtle.inp, Grizzly.inp,NSO.inp). Due Friday 3/09, 4:00pm.
*** *** *** *** *** Spring Break *** *** *** *** ***
21 3/19 Mon Computer-assisted Calculus, Delta methodPMJM Example, Bootstrap,Reading DERIVE:MLEs, Delta method, Bootstrap, Delta Answers, SAS Boostrap Code
22 3/21 Wed Mark-resight estimation from radio data, Bowden’s Estimator, Reading Program NOREMARK, JHE, Bowden’s estimator, andrea.hyp, andrea.mm
23 3/23 Fri Design of mark-resight surveys, Reading Program NOREMARK, Design features
24 3/26 Mon Line & point transect theory (Slide Show, data), Reading Dog chow experiment (if a nice day!)
25 3/28 Wed Line & point transect estimation,Reading Dog chow continued. Analyze data with DISTANCE
26 3/30 Fri Line & point transect estimationcontinued, Reading Dog chow continued. Discuss golf tee results Dog Chow 2007 zip files, Clustered Beer Cans zip files
27 4/02 Mon Bootstrap procedure in DISTANCE, special problems, Reading Program DISTANCE, Point transect data, Combined_D5.zip
28 4/04 Wed Review, questions, Reading Review, questions, Exam and evaluation handed out
29 4/06 Fri **** Final Exam Due **** Take-home (Files:  Jellies.zipCAGN.zip). Turn in exam and course evaluation to FWB office by 4:00pm.


Overview of Bayesian Estimation in MARK

bullet Data Dredging — Two Examples
bullet Goodness-of-fit of Product Multinomial Models
bullet Models Versus Full Reality
bullet The Set of Candidate Models

The following papers are to be published from a Program MARK workshop in Hungary, June, 1999.

bullet First Steps with Program MARK: Linear Models — Evan Cooch
bullet Exploring Ecological Relationships in Survival and Estimating Rates of Population Change Using Program MARK — Alan B. Franklin
bullet The Robust Design for Capture-Recapture Studies: Analysis using Program MARK — William L. Kendall
bullet Jointly Analyzing Live and Dead Encounters using MARK — Richard J. Barker and Gary C. White
bullet Advanced Features of Program MARK — Gary C. White, Kenneth P. Burnham, and David R. Anderson

The following papers are published in the proceedings of Euring 1997 in Bird Study Volume 46.

bullet Understanding Information Criteria for Selection among Capture-Recpature or Ring Recovery Models— David R. Anderson and Kenneth P. Burnham
bullet General Strategies for the Collection and Analysis of Ringing Data — David R. Anderson and Kenneth P. Burnham
bullet Program MARK: Survival Estimation from Populations of Marked Animals — Gary C. White and Kenneth P. Burnham<

Dr. Nigel G. Yoccoz, Department of Arctic Ecology, Norwegian Institute for Nature Research (NINA), Polar Environmental Centre, N-9296 Troms�, NORWAY, presented a slide show in the workshop session on the multinomial likelihood at the Euring 2000 meeting.

There was once a group of Statisticians and a group of Engineers riding together on a train to joint meetings. All the Engineers had tickets, but the Statisticians only had one ticket between them. Inquisitive by nature, the Engineers asked the Statisticians how they were going to get away with such a small sample of tickets when the conductor came through. The Statisticians said, “Easy. We have methods for dealing with that.”

Later, when the conductor came to punch tickets, all the Statisticians slipped quietly into the bathroom. When the conductor knocked on the door, the head Statistician slipped their one ticket under the door thoroughly fooling the layman conductor.

After the joint meetings were over, the Statisticians and the Engineers again found themselves on the same train. Always quick to catch on, the Engineers had purchased one ticket between them. The Statisticians (always on the cutting edge) had purchased NO tickets for the trip home. Confused, the Engineers asked the Statisticians “We understand how your methods worked when you had one ticket, but how can you possibly get away with no tickets?” “Easy,” replied the Statisticians smugly, “we have different methods for dealing with that situation.”

Later, when the conductor was in the next car, all the Engineers trotted off to the bathroom with their one ticket and all the Statisticians packed into the other bathroom. Shortly, the head Statistician crept over to where the Engineers were hiding and knocked authoritatively on the door. As they had been instructed, the Engineers slipped their one ticket under the door. The head Statistician took the Engineers’ one and only ticket and returned triumphantly to the Statistician group. Of course, the Engineers were subsequently discovered and publicly humiliated.

MORAL OF THE STORY. Do not use statistical methods unless you understand the principles behind them.

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Last Modified: April 09, 2007