An article by Ian Boyd from this week’s Science argues that there is a systemic problem of researchers under acknowledging the limits of model prediction:
Many of today’s ecological models have excessively optimistic ambitions to predict ecosystem and population dynamics.
And:
The main models are general population models (16) and data-driven, heuristic, ecosystem models (17,18), which are rarely validated and often overparameterized.
The good news is that:
Some recent studies (3–5)—including that by Mougi and Kondoh (6) on page 349 of this issue—help to specify where the limits of prediction may lie.
Initially, I thought that these articles (3-6) might be the answer that I’ve been searching for, but it seems that Dai et al. (2012), Allesina and Tsang (2012; see also here) and Mougi and Kondoh (2012) are examples of well derived models for specific questions, not rules for deciding how complex is too complex for a general range of ecological questions. Liu et al. (2011) is a general result, but asks a different question in speaking to the difficulty of controlling real complex systems.
Ian Boyd’s article raises more questions than answers, but it draws attention to an important question, which is highly worthwhile in-of-itself.
Just Simple Enough: The Art of Mathematical Modelling 