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
How the simulation works
3.3.2
Predict first
3.3.3
Getting started
3.3.4
Measure your hunting efficiency
3.4
Questions
3.5
Details about colours (optional)
3.6
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
Worked example
6.2.1
Inputs
6.2.2
Steps
6.2.3
Output and interpretation
6.3
Your task
6.4
Simulations in R
6.4.1
Example stochastic trajectories
6.5
Things to try
6.6
Questions
6.7
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
Notation
9.3
Worked example
9.4
Your task
9.4.1
Life table
9.4.2
Survivorship curves
9.5
Questions
9.6
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
10.4.1
What the projection does
10.4.2
Does the starting population matter?
10.4.3
Growth rate and stable stage distribution
10.5
Elasticity
10.6
In silico experiments: changing the life cycle
10.7
Your investigation
10.7.1
An evolutionary experiment (homework, optional)
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
The life history
11.3.2
Post-breeding census
11.3.3
Pre-breeding census
11.3.4
Comparing the two
11.4
A common mistake
11.5
Your task
11.6
Takeaways
12
Life Table Response Experiments
12.1
Background
12.2
Learning outcomes
12.3
Set up
12.4
A worked example
12.4.1
Comparing matrices
12.4.2
Sensitivity: how much does
\(\lambda\)
change when one element changes?
12.4.3
Contributions from individual matrix elements
12.4.4
Interpreting the contributions
12.5
Retrospective vs prospective analyses
12.6
Your task
12.7
Takeaways
13
How many eggs should a bird lay?
13.1
Background
13.2
Before you start: predict
13.3
Your task
13.4
Assumptions
13.5
Questions
13.6
Takeaways
14
Trade-offs and the declining force of selection
14.1
Background
14.2
Before you start: predict
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
Learning outcomes
15.2
What is a matrix population model?
15.3
The projection interval (
\(\Delta t\)
)
15.4
The population vector
15.5
The projection matrix
15.6
A simple 3-stage model
15.7
Projection: projecting
\(\mathbf{n}_t\)
forward
15.8
Define vital rates and build
\(\mathbf{A}\)
15.9
Define an initial population vector
\(\mathbf{n}_0\)
15.10
One-step projection
15.11
Project for many years (deterministic)
15.11.1
Plot totals and stage trajectories
15.12
Long-term growth rate (
\(\lambda\)
)
15.13
Simulation: adding stochasticity and management actions
15.13.1
Writing the simulation once, as a function
15.13.2
One simulated trajectory (no culling)
15.13.3
Add a culling intervention
15.13.4
Many replicates (uncertainty bands)
15.14
Your task
15.14.1
Exercise 1: Projection interval sanity check
15.14.2
Exercise 2: Change one vital rate and re-run projections
15.14.3
Exercise 3: Culling as management
15.14.4
Exercise 4: Contraception treatment (reduced fertility)
15.14.5
Exercise 5 (optional): Environmental stochasticity
15.15
Takeaways
15.16
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
16.4
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
Your task
17.2.1
The model and its assumptions
17.3
Part 1: A simple model
17.3.1
The gene pool
17.3.2
Projecting allele frequency over one time step
17.4
Part 2: Simulation of allele frequency through time
17.5
Part 3: Many populations
17.5.1
Rare alleles and new mutations
17.6
Part 4: Bottlenecks and founder effects
17.6.1
Founder effects (optional)
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 (10 minutes)
18.5
Takeaways
19
Writing Short Answers
19.1
What examiners look for
19.2
Paragraph structure
19.2.1
A worked example
19.3
Mark three answers yourself
19.3.1
Answer A
19.3.2
Answer B
19.3.3
Answer C
19.4
Now write your own (15 minutes)
19.5
Takeaways
20
Heritability from a linear regression
20.1
Background
20.2
Before you start: predict
20.3
Your Task
20.3.1
Estimating heritability (15 minutes)
20.3.2
Comparative Analysis (10 minutes)
20.3.3
Assumptions (10 minutes)
20.4
Takeaways
Part 4: Interactions Between Species and Community Structure
21
Lotka-Volterra competition
21.1
Background: the model and its isoclines
21.2
Key idea
21.3
Your task
21.3.1
The questions
21.4
Takeaways
22
Lotka-Volterra predator-prey dynamics
22.1
Background
22.2
Key idea
22.3
Your task
22.4
Optional extras
22.5
R-version
22.5.1
References
22.6
Takeaways
23
Continuous-time Lotka-Volterra Predator-Prey Model in R
23.1
Background
23.2
What you should see
23.3
Your task
23.3.1
Step 0: Create a new script
23.3.2
Step 1: Load Necessary Libraries
23.3.3
Step 2: Define the Model
23.3.4
Step 3: Set Parameters and Initial Conditions
23.3.5
Step 4: Simulate the Model
23.3.6
Step 5: Visualise the Dynamics
23.3.7
Questions
23.3.8
Conclusion
23.4
Takeaways
24
Discrete-time Lotka-Volterra Predator-Prey Model in R
24.1
Key idea
24.2
Introduction
24.3
Step 1: Define the Model
24.4
Step 2: Set Parameters and Initial Conditions
24.5
Step 3: Simulate the Model
24.6
Step 4: Visualise the Dynamics
24.6.1
Population Dynamics Over Time
24.6.2
Phase Plot with ZNGIs
24.7
Your task
24.8
Conclusion
24.9
Takeaways
Part 5: Animal behaviour, altruism and sexual selection
25
Game theory: Hawks and doves
25.1
Background
25.2
Worked example
25.2.1
Inputs
25.2.2
Steps
25.2.3
Output and interpretation
25.3
Your task
25.4
The payoff table
25.4.1
What does theory predict?
25.4.2
Hypotheses
25.4.3
Instructor setup (before class)
25.4.4
How today’s data collection works
25.4.5
GAME ONE: repeated partner
25.4.6
GAME TWO: changing partners every round
25.4.7
Watching the results live
25.5
Summarise and discuss results
25.6
Acknowledgement
25.7
Takeaways
25.7.1
Optional: running a live dashboard in R
Part 6: Solutions/answers
26
Solutions and “take-home” messages
26.1
How to use this section
26.2
Solutions: The blind watchmaker
26.3
Solutions: Bug hunt camouflage
26.4
Solutions: Geometric growth
26.5
Solutions: Estimating Population Growth Rate
26.6
Solutions: Stochastic population growth
26.6.1
Things to try
26.6.2
Questions
26.7
Solutions: Basic logistic population growth
26.7.1
Graph 1: Population size through time
26.7.2
Graph 2: Per capita growth rate vs. population size
26.7.3
Reflection
26.8
Solutions: Deeper into logistic growth
26.8.1
Relationship between Logistic and Exponential growth equations
26.8.2
Task 1: Type of dynamics depends on
\(r_m\)
26.8.3
Task 2: Per capita growth rate vs. population size (Figure 2)
26.8.4
Task 3: Population growth rate vs. population size (Figure 3)
26.8.5
Task 4: Comparison with exponential growth
26.8.6
Task 5: Time lag
26.8.7
\(N_{t+1}\)
vs.
\(N_t\)
26.8.8
Chaotic dynamics
26.9
Solutions: Life tables and survivorship types
26.9.1
Answers to the questions
26.10
Solutions: Matrix population modelling
26.10.1
Model answers for the matrix modelling practical
26.11
Solutions: Pre- and Post-reproduction census
26.12
Solutions: Life Table Response Experiments
26.13
Solutions: How many eggs should a bird lay?
26.14
Solutions: Trade-offs and the force of selection
26.15
Solutions: Matrix Population Models (MPMs): Projection and Simulation
26.16
Solutions: Hardy-Weinberg equilibrium
26.16.1
Problem 1
26.16.2
Problem 2
26.16.3
Problem 3
26.16.4
Problem 4
26.17
Solutions: Gene pool model
26.17.1
Part 2: Drift through time
26.17.2
Part 3: Many populations
26.17.3
Part 4: Bottlenecks and founder effects
26.17.4
Conclusions
26.18
Solutions: Neutral or adaptive evolution in humans
26.18.1
Suggested placements
26.18.2
Gene flow between populations
26.18.3
Discussion questions
26.19
Solutions: Heritability
26.20
Solutions: Lotka-Volterra competition
26.21
Solutions: Lotka-Volterra predator-prey dynamics
26.22
Solutions: Continuous-time Lotka-Volterra predator-prey model in R
26.23
Solutions: Discrete-time Lotka-Volterra predator-prey model in R
26.24
Solutions: Game theory: Hawks and doves
26.25
Solutions: The legend of Ambalappuzha
26.26
Solutions: From population biology to fitness
26.27
Solutions: From plain English to a matrix model
26.28
Solutions: Continuous traits from discrete genes
27
Results of the hawk-dove games
27.1
Game 1: the same partner for 15 rounds
27.1.1
Does being hawkish pay with a fixed partner?
27.2
Game 2: a new partner every round
27.3
Comparing the two games
Part 7: Appendix - extras
28
Exponential growth in detail
28.0.1
Nomenclature
28.1
Discrete time model
28.1.1
Calculating N for any future time point
28.1.2
Applying the model
28.2
Real-World Application: Breeding Pairs of Merlin (Falco columbarius)
28.2.1
Observations and Context
28.2.2
Applying the Exponential Growth Model
28.3
Continuous time model
28.3.1
Doubling time
28.3.2
Changing the time scale
28.3.3
Zero population growth
29
The legend of Ambalappuzha
29.1
Animals/plants, not grains of rice
29.1.1
Quick exercise (optional)
29.1.2
Discussion prompts
29.2
Optional: Try these calculations in R
30
From population biology to fitness
30.1
An
in silico
experiment
30.2
The link to fitness
30.3
Introducing a trade-off
31
From plain English to a matrix model
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
Continuous traits from discrete genes
32.1
Background
32.2
Worked example
32.2.1
Inputs
32.2.2
Steps
32.2.3
Output and interpretation
32.3
Your task
32.4
Takeaways
33
Building a phylogenetic tree
33.1
Background
33.2
Key idea
33.3
Worked example
33.4
Your task
33.5
Takeaways
Published with bookdown
BB512 - Population Biology and Evolution
Part 7: Appendix - extras