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
2.3.1
Inputs
2.3.2
Steps
2.3.3
Output and interpretation
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
3.5.1
Inputs
3.5.2
Steps
3.5.3
Output and interpretation
3.6
Details about colours (optional)
3.7
Takeaways
Part 2: Population Growth Models
4
Geometric growth
4.1
Background
4.2
Worked example
4.2.1
Inputs
4.2.2
Steps
4.2.3
Output and interpretation
4.3
Your task
4.4
Questions
4.5
Takeaways
5
Estimating Population Growth Rate
5.1
Background
5.2
Worked example
5.2.1
Inputs
5.2.2
Steps
5.2.3
Output and interpretation
5.3
Your task
5.4
Questions
5.5
Takeaways
6
Stochastic population growth
6.1
Background
6.2
Your task
6.3
Simulations in R
6.3.1
Example stochastic trajectories
6.4
Things to try
6.5
Questions
6.6
Takeaways
7
Basic logistic population growth
7.1
Background
7.2
Worked example
7.2.1
Inputs
7.2.2
Steps
7.2.3
Output and interpretation
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
8.2.1
Inputs
8.2.2
Steps
8.2.3
Output and interpretation
8.3
Your Task
8.4
Questions
8.5
Takeaways
9
Life tables and survivorship types
9.1
Background
9.2
Worked example
9.2.1
Inputs
9.2.2
Steps
9.2.3
Output and interpretation
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
Your task
10.3
Using R for matrix modelling
10.3.1
Package setup
10.4
Projecting the population
10.5
Elasticity
10.6
Life table response experiment (LTRE)
10.7
Your turn…
10.7.1
An evolutionary experiment
10.7.2
Questions
10.8
Takeaways
11
Pre- and Post-reproduction census
11.1
Background
11.2
Learning outcomes
11.3
Worked example
11.3.1
Inputs
11.3.2
Steps
11.3.3
Output and interpretation
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
13.2.1
Inputs
13.2.2
Steps
13.2.3
Output and interpretation
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
14.2.1
Inputs
14.2.2
Steps
14.2.3
Output and interpretation
14.3
Your task
14.4
Exploring different life history strategies
14.5
Questions
14.6
Takeaways
15
Matrix Population Models (MPMs): Projection and Simulation
15.1
What is a matrix population model?
15.2
The projection interval (\(\Delta t\))
15.3
The population vector
15.4
The projection matrix
15.5
A simple 3-stage model
15.6
Projection: projecting \(\mathbf{n}_t\) forward
15.7
Define vital rates and build \(\mathbf{A}\)
15.8
Define an initial population vector \(\mathbf{n}_0\)
15.9
One-step projection
15.10
Project for many years (deterministic)
15.10.1
Plot totals and stage trajectories
15.11
Long-term growth rate (\(\lambda\))
15.12
Simulation: adding stochasticity and management actions
15.12.1
One simulated trajectory (no culling)
15.12.2
Add a culling intervention
15.12.3
Many replicates (uncertainty bands)
15.13
Student exercises (for a class handout)
15.13.1
Exercise: Projection interval sanity check
15.13.2
Exercise: Change one vital rate and re-run projections
15.13.3
Exercise: Culling as management
15.13.4
Exercise: Contraception treatment (reduced fecundity)
15.14
Notes and extensions
Part 3: Population Genetics and Evolution
16
Hardy-Weinberg equilibrium
16.1
Background
16.2
Assumptions of Hardy-Weinberg Equilibrium
16.3
Worked example
16.3.1
Inputs
16.3.2
Steps
16.3.3
Output and interpretation
16.4
Your task
16.4.1
Problem #1.
16.4.2
Problem #2.
16.4.3
Problem #3.
16.4.4
Problem #4.
16.5
Takeaways
17
The Gene Pool Model
17.1
Background
17.2
Worked example
17.2.1
Inputs
17.2.2
Steps
17.2.3
Output and interpretation
17.3
Your task
17.4
A simple model
17.4.1
The gene pool
17.4.2
Projecting allele frequency over one time step.
17.5
Simulation of allele frequency through time
17.6
Bottlenecks
17.7
Conclusions
17.8
Takeaways
18
Neutral or Adaptive Evolution in Humans: What Drives Evolution of Our Traits?
18.1
Background
18.2
Key idea
18.3
Your task (30 minutes)
18.3.1
The traits
18.4
Discussion (Timing: 15 minutes)
18.5
Takeaways
19
Heritability from a linear regression
19.1
Background
19.2
Worked example
19.2.1
Inputs
19.2.2
Steps
19.2.3
Output and interpretation
19.3
Your Task
19.3.1
Estimating heritability (15 minutes)
19.3.2
Comparative Analysis (10 minutes)
19.3.3
Assumptions (10 mins)
19.4
Takeaways
Part 4: Interactions Between Species and Community Structure
20
Lotka-Volterra competition
20.1
Key idea
20.2
Your task
20.2.1
The questions
20.3
Takeaways
21
Lotka-Volterra predator-prey dynamics
21.1
Background
21.2
Key idea
21.3
Your task
21.4
Optional extras
21.5
R-version
21.5.1
Reference
21.6
Takeaways
22
Continuous-time Lotka-Volterra Predator-Prey Model in R
22.1
Background
22.2
What you should see
22.3
Your task
22.3.1
Step 0: Create a new script
22.3.2
Step 1: Load Necessary Libraries
22.3.3
Step 2: Define the Model
22.3.4
Step 3: Set Parameters and Initial Conditions
22.3.5
Step 4: Simulate the Model
22.3.6
Step 5: Visualise the Dynamics
22.3.7
Questions
22.3.8
Conclusion
22.4
Takeaways
23
Discrete-time Lotka-Volterra Predator-Prey Model in R
23.1
Learning outcomes
23.2
Key idea
23.3
Introduction
23.4
Step 1: Define the Model
23.5
Step 2: Set Parameters and Initial Conditions
23.6
Step 3: Simulate the Model
23.7
Step 4: Visualise the Dynamics
23.7.1
Population Dynamics Over Time
23.7.2
Phase Plot with ZNGIs
23.8
Conclusion
23.9
Your task
23.10
Takeaways
Part 5: Animal behaviour, altruism and sexual selection
24
Game theory: Hawks and doves
24.1
Background
24.2
Worked example
24.2.1
Inputs
24.2.2
Steps
24.2.3
Output and interpretation
24.3
Your task
24.4
The payoff table
24.4.1
Hypotheses
24.4.2
GAME ONE
24.4.3
GAME TWO
24.5
SUMMARISE RESULTS
24.6
Acknowledgement
24.7
Takeaways
Part 6: Solutions/answers
25
Solutions and “take-home” messages
25.1
How to use this section
25.2
Solutions: The blind watchmaker
25.3
Solutions: Bug hunt camouflage
25.4
Solutions: Geometric growth
25.5
Solutions: Estimating Population Growth Rate
25.6
Solutions: Stochastic population growth
25.7
Solutions: Basic logistic population growth
25.8
Solutions: Deeper into logistic growth
25.8.1
Relationship between Logistic and Exponential growth equations
25.8.2
Type of dynamics depends on
\(r_m\)
.
25.8.3
You can obtain parameters from graphs
25.8.4
Time lag
25.8.5
Chaotic dynamics
25.9
Solutions: Life tables and survivorship types
25.10
Solutions: Matrix population modelling
25.11
Solutions: How many eggs should a bird lay?
25.12
Solutions: Trade-offs and the force of selection
25.13
Solutions: Hardy-Weinberg equilibrium
25.13.1
Problem 1
25.13.2
Problem 2
25.13.3
Problem 3
25.13.4
Problem 4
25.14
Solutions: Gene pool model
25.14.1
Discussion questions
25.14.2
Bottlenecks
25.14.3
Broader questions
25.15
Solutions: Neutral or adaptive evolution in humans
25.16
Solutions: Heritability
25.17
Solutions: Lotka-Volterra competition
25.18
Solutions: Lotka-Volterra predator-prey dynamics
25.19
Solutions: The legend of Ambalappuzha
25.20
Solutions: From population biology to fitness
26
Results of the hawk-dove games
26.0.1
Game 2 - different opponents
Part 7: Appendix - extras
27
Exponential growth in detail
27.0.1
Nomenclature
27.1
Discrete time model
27.1.1
Calculating N for any future time point
27.1.2
Applying the model
27.2
Real-World Application: Breeding Pairs of Merlin (Falco columbarius)
27.2.1
Observations and Context
27.2.2
Applying the Exponential Growth Model
27.3
Continuous time model
27.3.1
Zero population growth
28
The legend of Ambalappuzha
28.1
Animals/plants, not grains of rice
28.1.1
Quick exercise (optional)
28.1.2
Discussion prompts
28.2
Optional: Try these calculations in R
29
From population biology to fitness
29.1
An
in silico
experiment
29.2
The link to fitness
29.3
Introducing a trade-off
30
From plain English to a matrix model
30.1
Background
30.2
Worked example
30.2.1
Inputs
30.2.2
Steps
30.2.3
Output and interpretation
30.3
Your task
30.4
Takeaways
31
Continuous traits from discrete genes
31.1
Background
31.2
Worked example
31.2.1
Inputs
31.2.2
Steps
31.2.3
Output and interpretation
31.3
Your task
31.4
Takeaways
32
Building a phylogenetic tree
32.1
Background
32.2
Key idea
32.3
Your task
32.4
Takeaways
Published with bookdown
BB512 - Population Biology and Evolution
Part 7: Appendix - extras