1. – the good, the bad, the ugly are all possible outcomes
Big models in ecology
– the good, the bad, the ugly are all possible outcomes
Why are we posing questions about big models?
Are they inherently problematic?
Are there various kinds of “big,” with different challenges?
Who dares to make big models?
How do we know if they are good?
Good in principle
Good in implementation = debugged, user-friendly, documented,
maintained).
What do models, and, particularly, big models, achieve?
How do we know when we get there?

2. In what ways can a model be big?
Conceptually = including many processes (real math, not a “conceptual model”)
In data hunger -- many inputs needed, legitimately
Mathematically (= computationally)
Even if simple to state  hard to solve
(E.g., box-packing problem; stiff DEs or singular boundary conditions)
From brute-force, inelegant formulation
In math – e.g., going numerical when analytical solutions exist
In coding – poor nesting, poor or excessive subroutine calls
Types of math
Algebraic and transcendental equations
Differential or finite-difference equations
Other algorithms, such as genetic algorithms for optzn.
Combinations of complexity: crossing multiple scales of space or time
Leading to mathematical or conceptual complexity
E.g., stiff DEs

3. Why make a big model. What are big questions
Why make a big model? What are big questions? What are various purposes of models?
Holy grail: prediction - for management, extrapolation of measurements
(e.g., FluxNet, Sib[2,3])…
Need either pure physics (transport relations, thermodynamics, …)
or robust biological laws (e.g., Farquhar-von Caemmerer-Berry)
Including optimization as a goal- deficit irrigation
Including inverse use, for determining processes or parameters
(SEBAL, LAI-2000, Asner land cover, …)
Partial prediction, too - biogeographic chaos
Hypothesis generation…and testing – most models
Including identification of important processes or parameters
Microclimate, not nutrients – M. Ball et al. (2001)
Pruning the number of experiments – very important use
Synthesis of knowledge - explaining "why" - Paltridge and Denholm
 This can lead to new questions, new hypotheses

4. How do we know if a model is good, especially a big model?
No single metric exists
Good intrinsic structure - accurate equations
Vs.: bugs - need for debugging  problem of intrinsic inability to debug
truly large models (David Parnas resigns from Star Wars, 1985)
Appropriate sensitivity to important processes, pruning of minor ones
Sensitivity analysis
Testing with real data - verification
I  O mapping must be good.
How good is good enough, for the model's purpose?
E.g., C fluxes - 20% (akin to accuracy of EC measts.)  what likely
error in integrated NEE? Could one detect global change?
Almost impossible for large models - no one can measure all the outputs
(or even inputs)
E.g., remote sensing models, such as SEBAL or land cover
Foolproofing - user can't mislead self (or it takes great talent to do so)
Good user interface
Solid documentation - and maintenance/support