BB512
1
Welcome to BB512
1.1
How to use this book
1.2
Parts overview
1.3
This website and other course materials
1.4
Expectations
1.5
Assessment
1.6
Instructors
1.7
Software
1.7.1
Excel
1.7.2
R and RStudio
1.8
Schedule
Part 1: Evolution by Natural Selection
2
The Blind Watchmaker
2.1
Background
2.1.1
Randomness and Selection
2.2
Learning outcomes
2.3
Worked example
2.4
Your task
2.4.1
The code
2.5
How the simulation works.
2.6
Questions
2.7
Takeaways
3
Bug hunt camouflage (NetLogo)
3.1
Background
3.2
Learning outcomes
3.3
Your task
3.3.1
Getting started
3.4
Questions
3.5
Worked example
3.6
Details about colours (optional)
3.7
Takeaways
Part 2: Population Growth Models
4
Geometric growth
4.1
Background
4.2
Worked example
4.3
Your task
4.4
Questions
4.5
Takeaways
5
Estimating Population Growth Rate
5.1
Background
5.2
Worked example
5.3
Your task
5.4
Questions
5.5
Takeaways
6
Stochastic population growth
6.1
Background
6.2
Worked example
6.3
Your task
6.4
Simulations in R
6.5
Questions
6.6
Takeaways
7
Basic logistic population growth
7.1
Background
7.2
Worked example
7.3
Your Task
7.3.1
Graph 1: Population Size Through Time
7.3.2
Graph 2: Per Capita Growth Rate vs. Population Size
7.3.3
Reflection on Graph 2
7.4
Takeaways
7.5
Questions
7.6
Optional: Do this in R.
8
Deeper into Logistic Growth
8.1
Background
8.1.1
Linking Logistic and Exponential Growth Models
8.2
Worked example
8.3
Your Task
8.4
Questions
8.5
Takeaways
9
Life tables and survivorship types
9.1
Background
9.2
Worked example
9.3
Your task
9.3.1
Life table
9.3.2
Survivorship curves
9.4
Questions
9.5
Takeaways
10
Matrix population modelling
10.1
Background
10.2
Worked example
10.3
Your task
10.4
Using R for matrix modelling
10.5
Projecting the population
10.6
Elasticity
10.7
Life table response experiment (LTRE)
10.8
Your turn…
10.8.1
An evolutionary experiment
10.8.2
Questions
10.9
Takeaways
11
Pre- and Post-reproduction census
11.1
Background
11.2
Learning outcomes
11.3
Worked example
11.4
Your task
11.5
Takeaways
12
Life Table Response Experiments
12.1
Introduction
12.2
Learning outcomes
12.3
Set up
12.4
A worked example
12.4.1
Comparing matrices
12.4.2
Contributions from individual matrix elements
12.5
Relationship with elasticity analysis
12.6
Your task
12.7
Takeaways
13
How many eggs should a bird lay?
13.1
Background
13.2
Worked example
13.3
Your task
13.4
Questions
13.5
Takeaways
14
Trade-offs and the declining force of selection
14.1
Background
14.2
Worked example
14.3
Your task
14.4
Exploring different life history strategies
14.5
Questions
14.6
Takeaways
Part 3: Population Genetics and Evolution
15
Hardy-Weinberg equilibrium
15.1
Background
15.2
Assumptions of Hardy-Weinberg Equilibrium
15.3
Worked example
15.4
Your task
15.4.1
Problem #1.
15.4.2
Problem #2.
15.4.3
Problem #3.
15.4.4
Problem #4.
15.5
Takeaways
16
The Gene Pool Model
16.1
Background
16.2
Worked example
16.3
Your task
16.4
A simple model
16.4.1
The gene pool
16.4.2
Projecting allele frequency over one time step.
16.5
Simulation of allele frequency through time
16.6
Bottlenecks
16.7
Conclusions
16.8
Takeaways
17
Neutral or Adaptive Evolution in Humans: What Drives Evolution of Our Traits?
17.1
Background
17.2
Worked example
17.3
Your task (30 minutes)
17.3.1
The traits
17.4
Discussion (Timing: 15 minutes)
17.5
Takeaways
18
Heritability from a linear regression
18.1
Background
18.2
Worked example
18.3
Your Task
18.3.1
Estimating heritability (15 minutes)
18.3.2
Comparative Analysis (10 minutes)
18.3.3
Assumptions (10 mins)
18.4
Takeaways
Part 4: Interactions Between Species and Community Structure
19
Lotka-Volterra competition
19.1
Worked example
19.2
Your task
19.2.1
The questions
19.3
Takeaways
20
Lotka-Volterra predator-prey dynamics
20.1
Background
20.2
Worked example
20.3
Your task
20.4
Optional extras
20.5
R-version
20.5.1
Reference
20.6
Takeaways
21
Continuous-time Lotka-Volterra Predator-Prey Model in R
21.1
Background
21.2
Worked example
21.3
Your task
21.3.1
Step 0: Create a new script
21.3.2
Step 1: Load Necessary Libraries
21.3.3
Step 2: Define the Model
21.3.4
Step 3: Set Parameters and Initial Conditions
21.3.5
Step 4: Simulate the Model
21.3.6
Step 5: Visualise the Dynamics
21.3.7
Questions
21.3.8
Conclusion
21.4
Takeaways
22
Discrete-time Lotka-Volterra Predator-Prey Model in R
22.1
Learning outcomes
22.2
Worked example
22.3
Introduction
22.4
Step 1: Define the Model
22.5
Step 2: Set Parameters and Initial Conditions
22.6
Step 3: Simulate the Model
22.7
Step 4: Visualise the Dynamics
22.7.1
Population Dynamics Over Time
22.7.2
Phase Plot with ZNGIs
22.8
Conclusion
22.9
Your task
22.10
Takeaways
Part 5: Animal behaviour, altruism and sexual selection
23
Game theory: Hawks and doves
23.1
Background
23.2
Worked example
23.3
Your task
23.4
The payoff table
23.4.1
Hypotheses
23.4.2
GAME ONE
23.4.3
GAME TWO
23.5
SUMMARISE RESULTS
23.6
Acknowledgement
23.7
Takeaways
Part 6: Solutions/answers
24
Solutions and “take-home” messages
24.1
How to use this section
24.2
Solutions: The blind watchmaker
24.3
Solutions: Bug hunt camouflage
24.4
Solutions: Geometric growth
24.5
Solutions: Estimating Population Growth Rate
24.6
Solutions: Stochastic population growth
24.7
Solutions: Basic logistic population growth
24.8
Solutions: Deeper into logistic growth
24.8.1
Relationship between Logistic and Exponential growth equations
24.8.2
Type of dynamics depends on
\(r_m\)
.
24.8.3
You can obtain parameters from graphs
24.8.4
Time lag
24.8.5
Chaotic dynamics
24.9
Solutions: Life tables and survivorship types
24.10
Solutions: Matrix population modelling
24.11
Solutions: How many eggs should a bird lay?
24.12
Solutions: Trade-offs and the force of selection
24.13
Solutions: Hardy-Weinberg equilibrium
24.13.1
Problem 1
24.13.2
Problem 2
24.13.3
Problem 3
24.13.4
Problem 4
24.14
Solutions: Gene pool model
24.14.1
Discussion questions
24.14.2
Bottlenecks
24.14.3
Broader questions
24.15
Solutions: Neutral or adaptive evolution in humans
24.16
Solutions: Heritability
24.17
Solutions: Lotka-Volterra competition
24.18
Solutions: Lotka-Volterra predator-prey dynamics
24.19
Solutions: The legend of Ambalappuzha
24.20
Solutions: From population biology to fitness
25
Results of the hawk-dove games
25.0.1
Game 2 - different opponents
Part 7: Appendix - extras
26
Exponential growth in detail
26.0.1
Nomenclature
26.1
Discrete time model
26.1.1
Calculating N for any future time point
26.1.2
Applying the model
26.2
Real-World Application: Breeding Pairs of Merlin (Falco columbarius)
26.2.1
Observations and Context
26.2.2
Applying the Exponential Growth Model
26.3
Continuous time model
26.3.1
Zero population growth
27
The legend of Ambalappuzha
27.1
Animals/plants, not grains of rice
27.1.1
Quick exercise (optional)
27.1.2
Discussion prompts
27.2
Optional: Try these calculations in R
28
From population biology to fitness
28.1
An
in silico
experiment
28.2
The link to fitness
28.3
Introducing a trade-off
29
From plain English to a matrix model
29.1
Background
29.2
Worked example
29.3
Your task
29.4
Takeaways
30
Continuous traits from discrete genes
30.1
Background
30.2
Worked example
30.3
Your task
30.4
Takeaways
31
Building a phylogenetic tree
31.1
Background
31.2
Worked example
31.3
Your task
31.4
Takeaways
Published with bookdown
BB512 - Population Biology and Evolution
Part 7: Appendix - extras