{"id":897,"date":"2017-05-30T17:35:09","date_gmt":"2017-05-30T17:35:09","guid":{"rendered":"http:\/\/sites.warnercnr.colostate.edu\/gwhite\/?page_id=897"},"modified":"2017-06-14T18:09:50","modified_gmt":"2017-06-14T18:09:50","slug":"fw663","status":"publish","type":"page","link":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/fw663\/","title":{"rendered":"FW663"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text]<\/p>\n

DistanceSampling.ppt<\/a>January 24, 2007<\/p>\n

FW663 — Sampling & Analysis of Vertebrate Populations<\/strong><\/p>\n

[\/vc_column_text][vc_tta_accordion][vc_tta_section title=”INSTRUCTORS” tab_id=”1496165679040-9ab2bd1a-2f6d”][vc_column_text]<\/p>\n\n\n\n\n\n
\"bullet\"<\/td>\n\u00a0<\/b>Gary C. White<\/a><\/b><\/td>\n<\/tr>\n
\"bullet\"<\/td>\n\u00a0\u00a0\u00a0\u00a0 Paul F. Doherty, Jr.<\/a><\/b><\/td>\n<\/tr>\n
\"bullet\"<\/td>\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0TA<\/b><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Study Sessions:<\/h3>\n

To be determined
\n[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”COURSE OBJECTIVES” tab_id=”1496165679060-cae14f29-dbc0″][vc_column_text]<\/p>\n

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:<\/p>\n

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

    [\/vc_column_text][\/vc_tta_section][vc_tta_section title=”COURSE CONTENT” tab_id=”1496166210749-1a38240f-ba9e”][vc_column_text]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).<\/p>\n

    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:<\/p>\n

    Sample data consisting of both marked and unmarked animals.<\/u><\/p>\n

    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.<\/p>\n

    Sample data consisting of only marked animals.<\/u><\/p>\n

    \n
    Survival estimation from tagged, banded, and radioed animals: Age and\/or time-specific survival rates, constant or variable effort.<\/dt>\n<\/dl>\n

    Sample data consisting of only unmarked animals.<\/u><\/p>\n

    Occupancy estimation: detection probabilities <1.<\/p>\n

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

    [\/vc_column_text][\/vc_tta_section][vc_tta_section title=”SOFTWARE” tab_id=”1496177073806-4c5a9266-0f55″][vc_column_text]The above methods often have comprehensive software<\/a> available (NOREMARK<\/a> \u00a0plus User’s Manual<\/a>; MARK<\/a> including the Euring 97<\/a> paper published in 1999, Evan Cooch’s Gentle Introduction<\/a> (continually updated), and papers from the 2nd IWMC in Hungary<\/a>; DISTANCE<\/a> plus User’s Guide<\/a>, RELEASE<\/a> plus the User’s Manual<\/a>; and CAPTURE<\/a> plus the User’s Manual<\/a>) and laboratory sessions will make extensive use of these packages in the Warner College of Natural Resources<\/a>\u00a0 PC Computer Laboratory<\/a>.[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”SOURCE MATERIALS” tab_id=”1496177075750-4ef01583-a74b”][vc_column_text]Major source materials will include (electronic copies of some of these available here):<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    \"bullet\"<\/td>\nBrownie, 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.<\/a><\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nBuckland, 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.<\/a><\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nBuckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. J. Thomas.\u00a0 2001.\u00a0 An introduction do distance sampling: estimating abundance of biological populations.\u00a0 Oxford University Press, Oxford, UK.\u00a0 432pp.<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nBuckland, 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.<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nBurnham, 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.<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nBurnham, K. P. 1993. A theory for combined analysis of ring recovery and recapture data. Pages 199-213 in<\/i> J.-D. Lebreton and P. M. North, eds. Marked Individuals in the Study of Bird Population. Birkhauser Verlag, Basel, Switzerland.<\/a><\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nBurnham, 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<\/a>.<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nBurnham, K. P., and D. R. Anderson.\u00a0 2002.\u00a0 Model selection and multimodel inference: a practical information-theoretic approach.\u00a0 2nd edition.\u00a0 Springer-Verlag, New York, New York, USA.\u00a0 488pp.<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nCooch, Evan, and G. C. White.\u00a0 1999.\u00a0 Program MARK: First Steps<\/a>.<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nLebreton, 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.<\/a><\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nEuring 2003.\u00a0 2004.\u00a0 Entire volume of Animal Biodiversity and Conservation 27.1:1-572.<\/a><\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nOtis, 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.<\/a><\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nPollock, K.H., J.D. Nichols, C. Brownie, and J.E. Hines. 1990. Statistical inference for capture-recapture experiments. Wildlife Monograph 107:1-97.<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nRexstad, E. A., and K. P. Burnham. \u00a01991. \u00a0User’s manual for interactive Program CAPTURE. \u00a0Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, Fort Collins, CO. \u00a029 pp.<\/a><\/big><\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nSeber, G. A. F. 1982. The estimation of animal abundance and related parameters, 2nd ed. Macmillan, New York, NY.<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nThomas, 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.<\/a><\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nThompson, W. L., G. C. White, and C. Gowan.\u00a0 1998.\u00a0 Monitoring vertebrate populations.\u00a0 Academic Press, San Diego, California, USA.\u00a0 365pp.<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nThompson, W. L. (ed.).\u00a0 2004.\u00a0 Sampling Rare or Elusive Species.\u00a0 Island Press, Washington, D.C., USA.\u00a0 429pp.<\/span><\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nWhite, 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.<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    [\/vc_column_text][\/vc_tta_section][vc_tta_section title=”GRADING” tab_id=”1496177076471-5311bd5d-4823″][vc_column_text]Grades will be assigned based on the following weights:<\/p>\n\n\n\n\n\n\n
    \"bullet\"<\/td>\n20% Quizzes (average with lowest 2 quiz scores discarded)<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\n30% Midterm I<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\n20% Midterm II<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\n30% Final Exam<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    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.<\/p>\n

    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.<\/p>\n

    Some Details:<\/p>\n

    FW663 is intensive, partially because it is offered on an accelerated basis.\u00a0 Each 4 hour period is 1.5 hrs. lecture, 0.5 hrs. recitation and 2 hrs. of laboratory.<\/p>\n

    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.<\/p>\n

    “Homework” is usually estimated as # credits times 2 hrs.\/week.\u00a0 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.\u00a0 Students that are statistically challenged or computer impaired should allow additional time.\u00a0 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.<\/p>\n

    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.”<\/p>\n

    Laboratory sessions try to facilitate team-work. Get used to working together to solve problems and gain understanding.<\/p>\n

    ALWAYS use ONLY your student ID (not your name) on all material to be turned in (e.g., Social Security Number<\/b> on quizzes, mid-term and final examinations).\u00a0 Using only your social security number aids in objective grading of the material.<\/p>\n

    [\/vc_column_text][\/vc_tta_section][vc_tta_section title=”SCHEDULE” tab_id=”1496183414053-16493438-a926″][vc_column_text]<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    Meet<\/u><\/td>\nDate<\/u><\/td>\nDay<\/u><\/td>\n\n

    \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Lecture \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0<\/u><\/p>\n<\/td>\n

    		\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0 Lab \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0<\/u><\/td>\n<\/tr>\n
    1<\/td>\n1\/24<\/td>\nWed<\/td>\nStatistics Review<\/a><\/big>, Binomial and Multinomial Coefficients<\/a>, Random Samples<\/a>, Expected Value of an Estimator<\/a><\/big><\/td>\nOrientation of CNR Computer Lab<\/td>\n<\/tr>\n
    2<\/td>\n1\/26<\/td>\nFri<\/td>\nBinomial Sampling and Binomial Distribution<\/a>, Binomial Likelihood Function<\/a>, Maximum Likelihood Estimation<\/a>, Likelihood Ratio Tests<\/a>,Reading<\/a><\/td>\nBinomial sampling<\/a>, SAS Input<\/a><\/td>\n<\/tr>\n
    3<\/td>\n1\/29<\/td>\nMon<\/td>\nMultinomial Distribution<\/a>, Confidence Intervals<\/a>, Future Course Directions<\/a>, MLE’s not in Closed Form<\/a><\/big>, Reading<\/a><\/td>\nML exercise with binomial distribution<\/a>, SAS Input<\/a><\/td>\n<\/tr>\n
    4<\/td>\n1\/31<\/td>\nWed<\/td>\nBand Recovery Models<\/a>, Parameter Index Matrix (PIM)<\/a><\/big>, Program MARK<\/a>, Reading<\/a><\/td>\nBrook trout<\/a>, Input data<\/a>, MARK DBF<\/a>, MARK FPT<\/a><\/td>\n<\/tr>\n
    5<\/td>\n2\/02<\/td>\nFri<\/td>\nSurvival estimation, band recoveries, AIC\u00a0<\/a>, AIC Lecture Notes<\/a>, AIC Handout<\/a>, Reading<\/a><\/td>\nSage grouse<\/a>, Input data<\/a>, MARK DBF<\/a>, MARK FPT<\/a><\/td>\n<\/tr>\n
    6<\/td>\n2\/05<\/td>\nMon<\/td>\nCovariates in survival estimation, design matrix\u00a0<\/a>, PIMs to Design Matrix<\/a>,\u00a0 Reading<\/a><\/td>\nGrouse data with design matrix<\/a>, MARK DBF<\/a>, MARK FPT<\/a><\/td>\n<\/tr>\n
    <\/td>\n<\/td>\nWed<\/td>\nMore on design matrices and model selection\u00a0<\/a>, Slide Show<\/a>, Color Slide Show<\/a>, Reading<\/a> Class exercise — design matrices<\/td>\nWork with MARK to understand design matrices<\/td>\n<\/tr>\n
    8<\/td>\n2\/09<\/td>\nFri<\/td>\nCormack-Jolly-Seber models\u00a0<\/a>, Reading<\/a><\/td>\nEuropean dipper data<\/a>, Input data<\/a>, MARK DBF<\/a>, MARK FPT<\/a><\/td>\n<\/tr>\n
    9<\/td>\n2\/12<\/td>\nMon<\/td>\nQuasi-likelihood, c-hat, extra-binomial variation\u00a0<\/a>, Reading<\/a><\/td>\nStarling\/DDT dosage data<\/a>,Input data<\/a>, MARK DBF<\/a>, MARK FPT<\/a><\/td>\n<\/tr>\n
    10<\/td>\n2\/14<<\/td>\nWed<\/td>\n\n

    Joint analysis of live & dead encs<\/a>., Reading<\/a>, Barker’s model, Pradel model<\/a><\/p>\n<\/td>\n

    		Goldeneyes joint live and dead encounters, pikeminnow Pradel model<\/a>, Barker input data<\/a>, Barker DBF<\/a>, Barker FPT<\/a>, Pradel input data<\/a>, Pradel DBF<\/a>, Pradel FPT<\/a><\/td>\n<\/tr>\n
    11<\/td>\n2\/16<\/td>\nFri<\/td>\nMonte Carlo simulations<\/a>, Reading<\/a>, Design and analysis of survival estimation experiments<\/td>\nDesign of starling\/DDT dosing<\/a>, RELEASE Input<\/a>, Example Simulation Input<\/a> and Output<\/a><\/td>\n<\/tr>\n
    12<\/td>\n2\/19<\/td>\nMon<\/td>\n\n

    Review, Questions, Hand out exam<\/a>, Files: terns.inp<\/a>, Mallards.inp<\/a>,NSO.inp<\/a><\/p>\n<\/td>\n<\/tr>\n

    13<\/td>\n2\/21<\/td>\nWed<\/td>\n*** Midterm I *** Take-home, Due Wednesday 2\/21, 4:00pm<\/td>\n<\/tr>\n
    14<\/td>\n2\/23<\/td>\nFri<\/td>\nSurvival estimation with radioed animals\u00a0\u00a0 (r<\/i> = p<\/i> = 1), logistic regression, individual covariates<\/a>, Reading<\/a><\/td>\nProgram MARK Mule deer fawn survival<\/a>, Input data<\/a>, MARK DBF<\/a>, MARK FPT<\/a>, and SAS logistic regression with body condition SAS Input<\/a>, SAS Data<\/a>.<\/td>\n<\/tr>\n
    15<\/td>\n2\/26<\/td>\nMon<\/td>\nCapture-recapture closed models, likelihoods<\/a>, closure, individual heterogeneity<\/a>, Reading<\/a><\/td>\nPrograms MARK and CAPTURE with capture-recapture data<\/a>, SAS Simulator<\/a>, Input data<\/a>,MARK DBF<\/a>, MARK FPT<\/a><\/td>\n<\/tr>\n
    16<\/td>\n2\/28<\/td>\nWed<\/td>\nRemoval estimation methods, model averaging, profile likelihoods<\/a>,Reading<\/a><\/td>\nProgram MARK with house mouse and electrofishing data<\/a>, coulombe.inp<\/a>, coulombe.dbf<\/a>,coulombe.fpt<\/a>, removal.inp<\/a>,removal.dbf<\/a>, removal.fpt<\/a>,Huggins Removal.inp<\/a>, Huggins Removal.dbf<\/a>, Huggins Removal.fpt<\/a><\/td>\n<\/tr>\n
    17<\/td>\n3\/02<\/td>\nFri<\/td>\nRobust design<\/a>, Multi-strata design<\/a>,Reading<\/a><\/td>\nProgram MARK Robust design data<\/a>, robust.inp<\/a>, robust.dbf<\/a>, robust.fpt<\/a>, mssurv.inp<\/a>, mssurv.dbf<\/a>, mssurv.fpt<\/a><\/td>\n<\/tr>\n
    18<\/td>\n2\/05<\/td>\nMon<\/td>\nVariance Components<\/a>, 2nd VC Write-up<\/a>, Hypothesis Testing<\/a>,Reading<\/a><\/td>\nReview, questions, additional work with robust design and multi-strata model,BurnhamExample.inp<\/a>,BurnhamExample.DBF<\/a>,BurnhamExample.FPT<\/a>,Mallards.inp<\/a>, Mallards.dbf<\/a>,Mallards.fpt<\/a><\/td>\n<\/tr>\n
    19<\/td>\n3\/07<\/td>\nWed<\/td>\nModel conceptualization exercise,Reading<\/a>, Review, questions<\/td>\nReview, questions, Exam handed out<\/td>\n<\/tr>\n
    20<<\/td>\n3\/09<\/td>\nFri<\/td>\n*** Midterm II *** Take-home<\/a> SeaTurtle.inp<\/a>, Grizzly.inp<\/a>,NSO.inp<\/a>). Due Friday 3\/09, 4:00pm.<\/td>\n<\/tr>\n
    *** *** *** *** *** Spring Break *** *** *** *** ***<\/td>\n<\/tr>\n
    21<\/td>\n3\/19<\/td>\nMon<\/td>\nComputer-assisted Calculus, Delta method<\/a>,\u00a0PMJM Example<\/a>, Bootstrap<\/a>,Reading<\/a><\/td>\nDERIVE:MLEs, Delta method, Bootstrap<\/a>, Delta Answers<\/a>, SAS Boostrap Code<\/a><\/td>\n<\/tr>\n
    22<\/td>\n3\/21<\/td>\nWed<\/td>\nMark-resight estimation from radio data, Bowden’s Estimator<\/a>, Reading<\/a><\/td>\nProgram NOREMARK, JHE, Bowden’s estimator<\/a>, andrea.hyp<\/a>, andrea.mm<\/a><\/td>\n<\/tr>\n
    23<\/td>\n3\/23<\/td>\nFri<\/td>\nDesign of mark-resight surveys<\/a>, Reading<\/a><\/td>\nProgram NOREMARK, Design features<\/a><\/td>\n<\/tr>\n
    24<\/td>\n3\/26<\/td>\nMon<\/td>\nLine & point transect theory (Slide Show<\/a>, data<\/a>), Reading<\/a><\/td>\nDog chow<\/a> experiment<\/a> (if a nice day!)<\/td>\n<\/tr>\n
    25<\/td>\n3\/28<\/td>\nWed<\/td>\nLine & point transect estimation,Reading<\/a><\/td>\nDog chow continued. Analyze data with DISTANCE<\/td>\n<\/tr>\n
    26<\/td>\n3\/30<\/td>\nFri<\/td>\nLine & point transect estimation<\/a>continued, Reading<\/a><\/td>\nDog chow continued. Discuss golf tee results Dog Chow 2007 zip files<\/a>, Clustered Beer Cans zip files<\/a><\/td>\n<\/tr>\n
    27<\/td>\n4\/02<\/td>\nMon<\/td>\nBootstrap procedure in DISTANCE, special problems, Reading<\/a><\/td>\nProgram DISTANCE, Point transect data<\/a>, Combined_D5.zip<\/a><\/td>\n<\/tr>\n
    28<\/td>\n4\/04<\/td>\nWed<\/td>\nReview, questions, Reading<\/a><\/td>\nReview, questions, Exam and evaluation handed out<\/td>\n<\/tr>\n
    29<\/td>\n4\/06<\/td>\nFri<\/td>\n**** Final Exam Due **** Take-home<\/a> (Files:\u00a0 Jellies.zip<\/a>,\u00a0 CAGN.zip<\/a>). Turn in exam and course evaluation to FWB office by 4:00pm.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

     <\/p>\n

    Overview<\/a> of Bayesian Estimation in MARK[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”ADDITIONAL READINGS” tab_id=”1496183414714-511c0d72-7315″][vc_column_text]<\/p>\n\n\n\n
    \"bullet\"<\/td>\nData Dredging — Two Examples<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
    \"bullet\"<\/td>\nGoodness-of-fit of Product Multinomial Models<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
    \"bullet\"<\/td>\nModels Versus Full Reality<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
    \"bullet\"<\/td>\nThe Set of Candidate Models<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    The following papers are to be published from a Program MARK workshop in Hungary, June, 1999.<\/p>\n\n\n\n\n\n\n\n
    \"bullet\"<\/td>\nFirst Steps with Program MARK: Linear Models<\/a> — Evan Cooch<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nExploring Ecological Relationships in Survival and Estimating Rates of Population Change Using Program MARK<\/a> — Alan B. Franklin<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nThe Robust Design for Capture-Recapture Studies: Analysis using Program MARK<\/a> — William L. Kendall<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nJointly Analyzing Live and Dead Encounters using MARK<\/a> — Richard J. Barker and Gary C. White<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nAdvanced Features of Program MARK<\/a> — Gary C. White, Kenneth P. Burnham, and David R. Anderson<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    The following papers are published in the proceedings of Euring 1997 in Bird Study Volume 46.<\/p>\n\n\n\n\n\n
    \"bullet\"<\/td>\nUnderstanding Information Criteria for Selection among Capture-Recpature or Ring Recovery Models<\/a>— David R. Anderson and Kenneth P. Burnham<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nGeneral Strategies for the Collection and Analysis of Ringing Data<\/a> — David R. Anderson and Kenneth P. Burnham<\/td>\n<\/tr>\n
    \"bullet\"<\/td>\nProgram MARK: Survival Estimation from Populations of Marked Animals<\/a> — Gary C. White and Kenneth P. Burnham<<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Dr. Nigel G. Yoccoz, Department of Arctic Ecology, Norwegian Institute for Nature Research (NINA), Polar Environmental Centre, N-9296 Troms\ufffd, NORWAY, presented a slide show<\/a> in the workshop session on the multinomial likelihood at the Euring 2000 meeting.[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”STORY” tab_id=”1496183415258-16e46753-913e”][vc_column_text]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.”<\/p>\n

    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.<\/p>\n

    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.”<\/p>\n

    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.<\/p>\n

    MORAL OF THE STORY. Do not use statistical methods unless you understand the principles behind them.[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”STATISTICAL HELP SITES” tab_id=”1496361440020-2f21f6fe-d808″][vc_column_text]<\/p>\n

    HyperStat Online<\/a><\/p>\n

    Statistics Education<\/a><\/p>\n

    Web Pages that Perform Statistical Calculations!<\/a><\/p>\n

    Stat-650<\/a> – Quantitative Methods for Resource Managers and Field Biologists, taught by Carl Schwarz at Simon-Fraser University.<\/p>\n

    Last Modified: April 09, 2007<\/span>[\/vc_column_text][\/vc_tta_section][\/vc_tta_accordion][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

    [vc_row][vc_column][vc_column_text] DistanceSampling.pptJanuary 24, 2007 FW663 — Sampling & Analysis of Vertebrate Populations [\/vc_column_text][vc_tta_accordion][vc_tta_section title=”INSTRUCTORS” tab_id=”1496165679040-9ab2bd1a-2f6d”][vc_column_text] \u00a0Gary C. White \u00a0\u00a0\u00a0\u00a0 Paul F. Doherty, Jr. \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0TA Study Sessions: To be determined [\/vc_column_text][\/vc_tta_section][vc_tta_section title=”COURSE OBJECTIVES” tab_id=”1496165679060-cae14f29-dbc0″][vc_column_text] FW663 is designed to include a balance …<\/p>\n

    FW663<\/span> Read More »<\/a><\/p>\n","protected":false},"author":117,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-897","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/pages\/897","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/users\/117"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/comments?post=897"}],"version-history":[{"count":25,"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/pages\/897\/revisions"}],"predecessor-version":[{"id":1200,"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/pages\/897\/revisions\/1200"}],"wp:attachment":[{"href":"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-json\/wp\/v2\/media?parent=897"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}