Week (dates) Lecture topic Lab topic Reading
Unit 1: Foundations of Bayesian inference
1 (Jan. 7 & 9) Intro to WILD6900 and statistical modeling in ecology Projects and directories/ Intro to R Markdown H&H chp. 1
2 (Jan. 14 & 15) Probability models and stochasticity Manipulating, tidying, & visualizing data in R H&H chp. 2-3
3 (Jan. 21 & 23) Principles of Bayesian inference Data simulation techniques/Informative priors H&H chp. 5
4 (Jan. 28 & 30) Implementing Bayesian models: MCMC samplers and JAGS Building simple samplers in R H&H chp. 7
5 (Feb. 4 & 6) Generalized linear models Introduction to JAGS/Regression analysis using JAGS H&H chp. 6, K&S chp. 3
6 (Feb. 11 & 13) Generalized linear models (cont.)/Hierarchical models Poisson GLMM for count data K&S chp. 3 & 4
Unit 2: Abundance and occupancy
7 (Feb. 18 & 20) Estimating indices abundance from count data State-space models K&S chp. 5
8 (Feb. 25 & 27) Estimating abundance from count data Basic N-mixture model & variations K&S chp. 12
9 (Mar. 3 & 5) Estimating occupancy from presence/absence data Static and dynamic occupancy models K&S chp. 13
10 (Mar. 10 & 12) Spring break - no class Spring break - no class
11 (Mar. 17 & 19) Estimating abundance: Closed-population capture-mark-recapture M0, Mt, Mb, Mh models K&S chp. 6
12 (Mar. 24 & 26) Estimating survival: Open-population capture-mark-recapture Cormack-Jolly-Seber models K&S chp. 7
13 (Mar. 31 & Apr. 2) Estimating abundance and survival: Open-population capture-mark-recapture Jolly-Seber models K&S chp. 10
Unit 3: Advanced models
14 (Apr. 7 & 9) Multi-state models Estimating movement rates using multi-state models K&S chp. 9
15 (Apr. 14 & 16) TBD
16 (Apr. 21 & 23) TBD