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WILD8390
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Spring 2019
Assignments
Solutions
week 1
Week 10
Week 3
Week 4
Week 5
Week 6
Week 8
Week 9
Week11
week12
Week13
week2
Week7
Lecture-lab schedule
0.1. Preliminaries/ setting up in R
0.1. Preliminaries
0.2. Getting set up in R
1. Introduction
Advanced Data Handling
Assignment (2 parts)
Introduction to R
Population models in R
Data handling
Population modeling
2. Statistical principles
AIC
Assignment
3- Abundance estimation
Assignment
Program DISTANCE
Sightability Models
Unmarked Distance Sampling
4. Occupancy estimation
Assignment
Chandler- unmarked
Other approaches
r-occupancy-1
r-occupancy-ii
r-occupancy-iii
sample-single
sample_multi
5. Capture-mark-recapture (CMR)
Assignment
Density estimation – III: Spatially explicit capture –recapture (SECR)
Goodness of Fit
Individual covariates
Lincoln- Petersen model
LP likelihood and simulation
Organizing data for input into RMark
RMark v MARK
Sampling Design
RMark and MARK equivalency for EE example
6. Spatial CMR
Assignment
Basic ideas of SCR
Chandler
Modeling utilization and density by landscape covariates
SECR
Simulating data
7. Age analysis and known fates
Analysis of harvest age proportions
Assignment
Known Fates in RMark
8. Open CMR: Cormack- Jolly-Seber
Assignment 8 b – Age specific analysis
Assignment 8a
CJS Models
CJS Models: Age structure
Review Questions for discussion -- no report needed
Parametric bootstrap
RMark dataframes
8.5 Tag recovery
Implementation in MARK
Implementation in MARK
Implementation in RMark
Review 1- One age tag recovery
Review 2- Two-age tag recovery
RMark
Tag recovery - Overview
9. Recruitment and Abundance Estimation
10 a- Recruitment, survival and abundance estimation in CMR
Assignment Part 2 : Robust design for recruitment
Recruitment estimation in RMark
Robust Design for Recruitment Estimation
_10.0 Robust Design Extensions and Multi-state models
Assignment Part 2 Multi-state models
Assignment- Part1
Multi-state models
multistate RD
Open RD
Review questions for discussion (no report needed)
Temporary emigration / availability
_11. Bayesian estimation
11.1. Introduction
11.2. Occupancy and CMR
11.1.1- Preliminaries
11.1.2. GLM and Mixed Models
11.1.3. Assignment Part 1
11.2.1. Building a Bayes model
11.2.2. Single-season occupancy model
11.2.3. CJS Random
11.2.4. Data augmentation
11.2.5. Assignment Part 2
11.2.1.1. Fixed
11.2.1.2. Random effects binomial model and Bayesian model selection
_12.Bayesian SCR
12.1. Spatial point-process models
12.2. Bayesian closed models
12.3. Bayesian open models
12.4. Assignment
_13.0. Hierarchical and integrated modeling
Assignment- State space / integrated models
Combined data structures
Joint live recapture and recovery
State-Space Models (Chapter 5 Kéry and Schaub 2012 )
_14. Wrap up/ presentations
_15. Presentations
Project Schedule
Archive
Spring 2016
Lecture-lab schedule
2.2- Statistical inference
Generalized linear models vs. Bayesian approaches
Review- Bayes
Review- sampling
Sample size determination and allocation
Week 13 Bayesian approaches to population estimation
Data Augmentation
Introduction to building Bayes models
Random effects binomial
Random Effects in CJS models
Review 1- Bayes review
Review 2 - Bayesian approaches to CJS
Simple (fixed effects) binomial model
Single-season occupancy a la Bayes
Week 7 Spatial CMR and Mark-resighting analysis
Basic ideas behind spatial CMR
Basic ideas of mark-resighting
Bayesian approaches
Chandler- SCR
Other issues
Review
RMark
SECR
IELN no intercept model
Review Exercises
Schedule and due dates
Worked exercises
10a Recruitment and Abundance
10b Robust design for recruitment
Week 1
Week 11a
Week 11b
Week 12a
Week 12b
Week 13a
Week 13b
Week 14 State space models
Week 2
Week 3a
Week 3b
Week 9a CJS
Week 9b
Week4
Week5
Week6
Week7
Week8
Schedule for Course Project
Previous examples
Final projects
Project Guidelines
Project presentations
Contact information- Office hours, etc.
Course Objectives
Course Policies
Course Topics
Grading
Software
R Simulation code
Simulation for study design
Textbooks and references
WILD8390
Week 13a
Fixed effect binomial model (constant p)
Random effect binomial model
Model selection
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