Likelihood Methods in Ecology

2009 Course Schedule

Monday

8:30 – 9:30 Lecture 1: An Introduction to Likelihood Estimation

9:30 – 10:10 Case Study: Distribution and Abundance of Tree Species Along Climate Gradients

10:10 – 10:30 Break

10:30 – 12:00 Lab 1: Calculating Likelihood, Likelihood Surfaces, and Likelihood Profiles [R Code]

12:00 – 1:30 Lunch

1:30 – 2:20 Lecture 2: Choosing Appropriate Likelihood and Probability Density Functions [Part 1] [Part 2]

2:20 – 3:10 Lab 2: Probability and Likelihood [R code – Probability] [R code – Likelihood]

[Dataset – Hmtab43.txt] [Dataset – Reef_fish.txt] [Dataset – Sapling_Growth.txt]

3:10 – 3:30 Break

3:30 – 5:00 Lab 2: Probability and Likelihood

Assigned Reading for Tuesday:

Gómez-Aparicio, L. and C. D. Canham. 2008. A neighborhood analysis of the allelopathic effects of the invasive tree Ailanthus altissima in temperate forests. Journal of Ecology 96:447-458

Tuesday

8:30 – 9:30 Lecture 3: Hypothesis Testing and Statistical Inference Using Likelihood: The Central Role of Models

9:30 – 10:10 Case Study 1: Neighborhood Models Of The Allelopathic Effects Of An Invasive Tree Species

10:10 – 10:30 Break

10:30 – 12:00 Lab 3: Functions in R [BC Sapling Growth Data.txt]

12:00 – 1:30 Lunch

1:30 – 2:20 Lecture 4: Parameter Estimation and Evaluation of Support

2:20 – 3:10 Lab 4: Global Optimization using Simulated Annealing and Genetic Algorithms

[R code – anneal] [R code – genoud]

3:10 – 3:30 Break

3:30 – 5:00 Lab 4: Global Optimization (cont.), and Local Optimization with Optim [R code – optim]

Assigned Reading for Wednesday:

Uriarte, M., E. M. Bruna, P. Rubim, M. Anciães, and I. Jonckheere. Effects of forest fragmentation on seedling recruitment of an understory herb: Assessing seed vs. safe-site limitation. Ecology, in press.

Bolker, B. 2008. Bestiary of Functions. Pp. 87-99 in Ecological Models and Data in R.

Wednesday

8:30 – 9:20 Lecture 5: Model Comparison and Evaluation: AIC and Akaike Weights, Multi-model Inference, Goodness of Fit, Bias

9:20 – 10:10 Case Study 2: Seed vs Safe-site Limitation in Heliconia acuminata

10:10 – 10:30 Break

10:30 – 12:00 Lab 5: Model Comparison and Evaluation in R [Excel file]

12:00 – 1:30 Lunch

1:30 – 2:20 Catching up: Discussion and Review

2:20 – 3:10 Lab 6: Inverse Modeling

3:10 – 3:30 Break

3:30 – 5:00 Lab 6: Inverse Modeling (continued)

Assigned Reading for Thursday:

Coates, K. D., C. D. Canham, and P. T. LePage. 2009. Above versus belowground competitive effects and responses of a guild of temperate tree species. Journal of Ecology 97:118-130.

Thursday

8:30 – 9:30 Lecture 6: Avoiding and Dealing with Problems with Your Data and Models: Sampling Design, Lack of Independence, Spatial Autocorrelation, Collinearity, Parameter Tradeoffs,…

9:30 – 10:30 Case Study 3: Neighborhood models of tree competition

10:30 – 11:00 Break

11:00 – 12:00 Cary Institute Seminar: Dr. Inez Ibanez (University of Michigan), Role of Plant-Soil Feedbacks on Species Response to Climate Change

12:00 – 1:30 Lunch

1:30 – 2:20 Lecture 7: Analysis of Categorical and Ordinal Data: Binomial and Logistic Regression

2:20 – 3:10 Lab 7: Developing your own binomial and logistic regression models in R

[R code – Logistic regression of windthrow data] [Damagedata.Rdata] [R code – binomial regression]

3:10 – 3:30 Break

3:30 – 5:00 Lab 8: Quantile regression in R using quantreg [R code – quantile regression] [Cade and Noon (2003) Quantile regression for ecologists]

[Koenker and Hallock (2000), Quantile regression, an Introduction], [Koenker (2006) Quantile Regression in R: A Vignette]

Assigned Reading for Friday:

Comita, L.S., M. Uriarte, J. Thompson, I. Jonckheere, C. D. Canham, and J. K. Zimmerman. Abiotic and biotic drivers of seedling survival in a hurricane-impacted tropical forest. Journal of Ecology 97:1346-1359.

Bolker, B. 2008. Standard Statistics Revisited. Pp. 298-315 in Ecological Models and Data in R.

Bolker, B. et al. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. TREE 24: 127-135

Friday

8:30 – 9:20 Lecture 8: Traditional Statistics Revisited

9:20 – 10:10 Case Study 4: Seedling survival in a tropical forest

10:10 – 10:30 Break

10:30 – 12:00 Lab 9 – Part 1: Statistics Revisited [code] [data]

Part 2: Fitting Hierarchical, Mixed-Effects Models using lme4

[lmer_lab.txt] [fruit_data.txt] [seedling_survival_data.txt]

12:00 – 1:30 Lunch

1:30 – 4:00 Wrap-up Discussion: Developing your own statistical tool-kit and philosophy