BIOL 7210 Evolutionary Ecology

Typically offered every other spring semester

(e.g., Spring of 2021, 2023, etc.)

This is a 3-credit hour course for advanced graduate students.

Course Summary

Biological hypotheses are cast in terms of statistical models throughout many fields within behavioral ecology, ecology, and evolutionary biology. With the advent of more complex statistical methods and their implementation in these fields, come further challenges and questions regarding how best to fit models to data that are often less than “ideal”. In this course, we will investigate a number of current “hot” research topics in Evolutionary Ecology, examine the leading hypotheses being tested by discussing the literature, and employ the statistical techniques ourselves. An emphasis will be placed on computational approaches to model fitting, model assessment, and parameter estimation and/or hypothesis testing. We will come away with both a broader and deeper knowledge of current avenues of research in Evolutionary Ecology programs as well as a working knowledge of the most advanced statistical methods being employed.

Student Learning Outcomes:

Students will think critically about primary scientific literature by breaking down and evaluating core components to a scientific paper in written assignments.
Students will use standard statistical techniques and demonstrate their proficiency by solving problems in lab exercises.
Students will learn to apply advanced statistical and computational techniques as demonstrated through individual projects.
Students will identify the current leading questions in evolutionary ecology research as demonstrated through pre-discussion written assignments and group discussion.
Students will demonstrate their biological and statistical knowledge through oral presentation of their own analyses.

Course Objectives:

Students will read and discuss primary literature on current questions in evolutionary ecology that will empower them with both conceptual and practical knowledge of the field.
Students will practice standard techniques for data analysis in evolutionary ecology using the R statistical program.
Students will implement advanced statistical and computational methods.
Students will conduct practical data analysis on datasets of their choosing and be mentored in the application of robust statistical and computational methods for evaluating biological questions.
Students will learn how to critically review papers and methods, with particular emphasis for identifying knowledge gaps in evolutionary ecology research.
Students will present research and analyses in both the written and oral formats.
Students will practice documenting and annotating code and data.

Materials

Required:
- Personal Laptop
- R software installed either as the R (https://cran.r-project.org/) or RStudio (https://www.rstudio.com/) programs

Course Schedule

Sample schedule from Spring 2023

Week 1

Wednesday: Introduction
- Evolutionary Ecology and a Frequentist refresh
Thursday: R
- Introduction/Refresher for R, matrices, etc.

Week 2

Tuesday: Paper discussion
- Data Exploration
  - Zuur et al. 2010
Wednesday: Paper discussion
- Frequentist statistics
  - Sections I and II only of Nakagawa & Cuthill 2007
  - (pp. 591 - 595)
Thursday: Paper discussion
- Language of evidence
  - Muff et al. 2022

Week 3

Tuesday: Paper discussion
- Variance
  - Mazer & Damuth 2001, ch. 1 in Evolutionary Ecology
- Repeatability
  - Lessells & Boag 1987
Wednesday: R
- Introduction to ICCs
Thursday: Paper discussion
- Animal Personality
  - Reale et al. 2007

Week 4

Tuesday: Paper discussion
- Animal Personality
  - Careau et al. 2008
Wednesday: Lecture
- Natural Selection, Multiple Linear Regression, and Splines
Thursday: Paper discussion
- Natural selection
  - Fairbairn & Reeve 2001, ch. 3 in Evolutionary Ecology

Week 5

Tuesday: Paper discussion
- Natural Selection
  - Wade & Kalisz 1990
Wednesday: Paper Discussion
- Natural Selection and PCA
  - Chong et al. 2018
Thursday: R
- Selection and Splines Part I

Week 6

Tuesday: R
- Selection and Splines Part II
Wednesday: Paper discussion
- Selection
  - Morrissey & Hadfield 2012
Thursday: Lecture
- Generalized Linear Model

Week 7

Tuesday: R
- GLMs
Wednesday: Lecture
- Generalized Additive Models
Thursday: R
- GAMs

Week 8

Tuesday: Lecture
- Linear mixed models, ML, & REML
Wednesday: R
- LMMs
Thursday: Lecture
- Introduction to quantitative genetics

Spring Break

Week 9

Tuesday: Paper discussion
- QG
  - Mazer & Damuth 2001, ch. 2 in Evolutionary Ecology
  - Wilson et al. 2010
Wednesday: R
- QG-LMM
Thursday: Paper discussion
- ‘Conditional’ ICC and h2
  - Wilson 2008
  - Wilson 2018

Week 10

Tuesday: Paper discussion
- Repeatability and heritability
  - Dohm 2002
Wednesday: Mid-term project student presentations
- DUE: Mid-term project R code & datasets
Thursday: Mid-term project student presentations

Week 11

Tuesday: Paper discussion
- Random regression
  - Nussey et al. 2007
  - Dingemanse & Dochtermann 2013
Wednesday: R
- Random regression
Thursday: Paper discussion
- Multivariate models: Trade-offs
  - Roff & Fairbairn 2007

Week 12

Tuesday: Paper discussion
- Multivariate models: Evolutionary (non-)responses to selection
  - van Tienderen & de Jong 1994
  - Morrissey et al. 2010
Wednesday: Paper discussion
- Random regression and trade-offs
  - Careau & Wilson 2017 ICB
Thursday: Lecture/R
- Part I: LMM p-values, confidence intervals, and LRT gold standards

Week 13

Tuesday: Lecture/R
- Part II: LMM p-values, confidence intervals, and LRT gold standards
Wednesday: Lecture
- Bayesian statistics
Thursday: R
- Priors and posteriors

Week 14

Tuesday: Paper discussion
- Bayesian Evolutionary Genetics
  - O’Hara et al. 2008
Wednesday: TBD
Thursday: R
- Frequentist and Bayesian GLMM

Week 15

Tuesday: Paper discussion
- Phylogenetic comparative method
  - Hadfield & Nakagawa 2010
Wednesday: R
- Bayesian GLMM
Thursday: R
- Final project co-working session

Week 16

Final project student presentations