Introductory principles
· Introduction; the role of models; science in wildlife ecology and management
o Intro to R for data handing and model
· Basics of dynamic modeling for populations
o Population models in R
· Statistical estimation
o General principles
o Maximum likelihood
§ Distributions, simulation in R
o AIC /model selection
o Bayesian estimation
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o Sampling / experimental design
§ Using R to design a study
Estimation of abundance, density, and and occupancy
· Overview of estimation
· Closed vs. Open estimation
· Count-based methods
· Distance estimation
· Capture mark-recapture
o Basic CMR
§ Organizing data for cmr
§ Basic analysis in R
o Multiple sample CMR
§ Organizing data for cmr
§ Basic analysis in R
o Mark-resighting
o Spatial CMR
· Occupancy estimation
o Organizing data
o Basic analysis in R
o Dealing with heterogeneity in detection and occupancy
o Estimation of abundance with occupancy models
Estimation of dynamic abundance/occupancy and transition probabilities
· Overview
· Methods based on age frequencies
· Nest success and telemetry (“known fates”)
· Tag recovery
· Open CMR (Cormack- Jolly –Seber)
· Estimation of abundance and recruitment
o Jolly-Seber
o Reverse time
· Robust design
· Dynamic (multi-season) occupancy analysis
Hierarchical modeling for population inference using OpenBUGS
· CMR applications—random effects
· Converting CMR problems to an occupancy framework via data padding
· Occupancy modeling- mixed effects, modeling abundance
· State- space and integrated models