The original proposal

Complex systems self-organize. Stuart Kaufman has simulated simple classes of complex systems and formally described how they tend to form low-order organized networks. George Mpistos has applied the same ideas to neural systems and found that, even with minimal increases in complexity system behavior becomes more realistic in simulations, but mathematically intractable. George Sugihara's analysis suggests that natural systems have complex dynamical behaviors that render their behavior nearly impossible to predict. If we are to truly understand natural systems beyond the equilibrium assumptions almost universal in the field (Lotka-Volterra, Ricker, Beverton-Holt, Hardy-Weinberg just for starters) we must consider the nature of complexity and apply its general properties to specific examples.
One of Mpitsos' intriguing findings relates to the behavior of neural networks. He simulated three neural nets; one without noise, one with noise, and one with noise and interactive nodes (is this right, George?). The noisy networks learned much faster than the un-noisy net. More interesting, when perturbed the un-noisy net behaved exactly as it did initially, while the noisy nets responded more quickly than they had the first time. George's explanation is that the state of the noisy nets incorporated information about the error structure of the process. As long as the perturbation did not greatly affect the error structure, the noisy nets started with a great deal of information they did not have to re-discover. Therefore they could more quickly arrive at a good solution.

I propose that this has direct relevance to natural populations. Over time, natural populations evolve and adapt to find a good solution to surviving in a noisy environment. This adaptation is embedded in their genome and in cultural or other epigenetic phenomena. They are analogous to George's neural nets with noise. When their environment experiences natural perturbations these populations can respond rapidly to find a new good solution. However, if the population's natural adaptive state is disturbed by simplifying the genome, reducing or eliminating behavioral variability, or introducing novel, unadapted genotypes then the ability of the population to respond to perturbation is reduced. This translates, in conservation biology lingo, to lower resilience. The explanation is that, by simplifying, information about the error structure of the system is lost. (There may also be other properties of neural networks that we can invoke related to behaviors with different numbers of nodes as an analog to simplified genome).

Another way the well-adapted population can be rendered less resilient is if the error structure of the system is changed. This can occur through rapid habitat alteration, changes in predation patterns, or rapid changes in environmental forcing factors (i.e., climate change).
I would like to explore this idea using salmon as a model system (any other suggestions for more tractable systems?). We could compare relatively undisturbed systems (Bristol Bay sockeye) with severely disturbed systems (Central Valley Chinook). We could look at newly established populations (Glacier Bay coho?) with populations believed to be in equilibrium with their environment (where?). Can we find indexes of variability for genetics, life cycle, habitat, climate? We can do simulations using some of the methodologies George M. has developed.
If these principles are truly common to all complex dynamical systems it could provide important insight for conservation planning of all sorts. It could help explain forest dynamics, population dynamics, ecosystem dynamics, dynamics of exploited populations. It would not predict specific outcomes, but might provide an index of risk or vulnerability, and suggest restoration measures. It might also give insight into how long it may take (generations) for resiliency to become reestablished once lost due to perturbation of either the population or the environment. It could also give insight into what may happen when (as is happening) both the population and the environment are being perturbed simultaneously.