Systems Ethology
Preface
1
Why Quantitative Population Ethology?
1.1
Maleable Tools for the Field Biologists
1.1.1
Tying simulations to field data
1.2
Simulating Millions of Organisms
1.2.1
Limitations of brute force computing
1.2.2
Loose coupling of model cascades
1.3
From Individual Behavior to Evolution
1.4
Discussion from QPE
2
Limitations of Classical Models
2.1
Mathematical Biology
2.2
Biology vs. Physics
2.3
Predator-Prey Models
2.4
Classical Differential Equation Models
2.4.1
Early Work: The Holling Predation Studies
2.4.2
Lotka-Volterra, Leslie matrices
2.4.3
Time and phase space
2.4.4
Stable cycles and chaos
2.4.5
Quadratic and Linear Equations in Optimization Research
2.4.6
Limitations
2.5
Life tables and competing risks
2.6
Synchronized generations
2.7
Time Driven Simulation Models
2.8
Population size: continuous or discrete
2.8.1
Time almost always one unit
2.8.2
Stochastic dynamic systems
2.8.3
Discrete time stochastic Petri nets
2.9
Event-driven Petri nets
3
Field Biologists and the Collection of Data {#field}}
4
Probabilistic Nature of Individual Behavior
4.1
Properties of an individual
4.2
Interactions among individuals in community
4.3
Field sampling process
4.4
Quantization of events/states
4.5
Quantization of time/space
4.6
Heisenberg uncertainty principle
4.7
Games of Skill and Chance
4.7.1
Backgammon
4.7.2
Bridge
4.7.3
Chess
5
Event-Driven Competing Risks Structure
5.1
Event-Driven Considerations for Living Systems
5.2
Minimization structure within, across individuals
5.3
Modelling event to event
5.4
Non-homogeneous Poisson process rebuilt at every event
5.5
Event, span, resolution
5.6
Future/immediate/pending events
5.7
Competing Risks for Life Events
5.7.1
Competing risk structure
5.7.2
Time depends on events
5.7.3
Event structure for individual
5.7.4
Five-dimensional parameterization
5.7.5
Handling immediate events
6
Simulation Mechanics
6.1
Choice of Modeling System
6.2
Cubic splines
6.2.1
Time to event based on data or intuition
6.2.2
Translating time to degree-days and back
6.3
Spatial components
6.3.1
Tridiagonal coordinate system
6.3.2
Sierpinski gasket
6.3.3
Icosohedron/triangular cylinder for objects
6.3.4
Smooth integration between triangles
6.4
Priority queues
6.4.1
Partial ordering of individuals based on next future event
6.4.2
Dynamic storage allocation
6.5
Pseudo-Randomization
6.5.1
Generalized feedback shift registers
6.5.2
Exponential random values
6.5.3
Sierpinski gasket: order, direction
6.5.4
Fractal nature of spatial search algorithms
6.6
Curse of Dimensionality
6.6.1
Calculations linear in number of individuals * ave. number of life events
6.6.2
Ramping up from hundreds to millions
6.6.3
Quasi-parallel computation
6.6.4
Efficient use of supercomputer clusters
6.6.5
Loosely connected communities
6.7
Future Considerations
7
Data Integration
7.1
Sample splitter to count insects
7.2
Organization of data in relational database
7.2.1
Access, PostgresQL
7.2.2
Beyond: H5(?) used for satellite images, microarray data
7.3
Tying data to events
8
Biology of Red Scale and its Parasitoids {# redscale}
8.1
Detailed life histories
8.1.1
Red Scale Life History
8.1.2
Aphytis Life History
8.2
Interaction events: endo-, ecto-, hyper-parasitism
8.3
Diurnal and degree-day effects; seasonal effects
8.4
Position in tree
8.4.1
Orange/branch/leaf
8.4.2
Exterior/main trunk
8.5
Search algorithms
8.5.1
Parasite for host
8.5.2
Host for substrate
8.5.3
Local vs. long distance search
8.5.4
Simulations
8.6
Simulations from QPE Paper
8.6.1
Model Span and Resolution
8.6.2
Model Results
9
Revisiting Wolff’s Wading Birds {# wolff}
10
Validation and Sensitivity Testing
10.1
Criteria for Model Development
10.1.1
Explained Variation
\(R^2\)
10.1.2
Attribute inclusion/exclusion significance level
10.1.3
Linear and non-linear equations
10.1.4
Parsimony
10.2
Model diagnostic tests
11
Reusable, Extensible Simulation Code
11.1
Tabular data
11.1.1
Organism Features
11.1.2
Organism Future Events
11.1.3
Interactions among Organisms
11.1.4
Organism profiles
11.1.5
Species life histories
11.1.6
Interaction processes
11.1.7
Spatial movement on icosohedron, large substrate structures
11.2
Event subroutines
11.2.1
Competing Risk Structure
11.2.2
Birth, death
11.2.3
Life stages
11.2.4
Search, feed
11.3
Bayesian logic programming
11.3.1
Combining Bayesian networks and ProLog
12
Summary and Conclusion
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Systems Ethology
Chapter 9
Revisiting Wolff’s Wading Birds {# wolff}