Course Description
Provides an exposure to key software packages used in fish and wildlife population analysis with emphasis on capture-mark- recapture, including generalized linear, random effects, and hierarchical models. Centered on the R programming language and other programs that are freely available via the Internet.
Additional Requirements for Graduate Students:
Graduate students will be expected to apply methods to analysis
of data obtained from their own studies, obtained from
colleagues, or derived from published archives. Additionally,
graduate students will be expected to provide a project report
in manuscript format and prepare and present online a
professional quality presentation describing the problem
background, data, statistical methods, and pertinent results.
Athena Title
Fish and Wildlife Software
Non-Traditional Format
This course will be taught 95% or more online.
Prerequisite
FANR 2010-2010L or STAT 2000 or STAT 2000E or BIOS 2010 or BIOS 2010E or BUSN 3000 or BUSN 3000E or BUSN 3000H
Semester Course Offered
Not offered on a regular basis.
Grading System
A - F (Traditional)
Course Objectives
1. Familiarity with key statistical packages used in analysis of mark-recapture and other data for fish and wildlife populations 2. Facility with the R programming language 3. Recognition of appropriate statistical tools for specific data problems in fish and wildlife population analysis, with emphasis on capture-mark-recapture data 4. Ability to apply the R statistical package to data manipulation and formatting for capture-mark-recapture analysis 5. Ability to use packages R, RMark, and MARK to appropriately analyze capture-mark-recapture data 6. Ability to correctly interpret program output for performing appropriate statistical tests and the valid computation of estimates of abundance, survival, and other parameters from mark-recapture data 7. Ability to extend basic capture-mark-recapture models to more complex situations, including random effects and hierarchical structure 8. Ability to appropriately display in tabular and graphical form results of analyses for use in presentations, reports, and publications
Topical Outline
• Introduction to computing using R • Using R to manage data • Introduction to Capture-Mark-Recapture models • Introduction to programs MARK and RMark • Programs MARK and RMark details • Age and cohort structure • Model fit and multi-model inference • Time and individual covariates; prediction • Tag recovery and live-dead models, Known fate models • Abundance estimation in closed CMR models • Abundance and recruitment estimation in open CMR models • Robust design for estimating abundance, survival, recruitment, and temporary emigration • Multi-state models and intro to simulation • Simulation and bootstrapping in R and MARK • Bayesian approaches for dealing with random effects and hierarchical structure