L-systems describe, they do not explain
Start with the string X. Replace every X with F[+X]F[-X]+X and every F with FF. Do it five times. Hand the result to a turtle that reads F as “go forward”, + and − as “turn”, and brackets as “remember and restore where one are”.
Four hundred and twenty-two line segments come out, and they look like a plant.
What is genuinely good about them
Lindenmayer introduced these in 1968 as a model of how filamentous organisms develop, and they have real virtues.
They capture self-similarity honestly. A plant’s branching repeats at several scales, and a rewriting rule is exactly a statement about a structure that contains copies of itself.
They are compact. Two rules and an iteration count produce arbitrarily complex structures, which matches the observation that a genome does not store a tree’s shape.
They are parametric. Adding parameters to the symbols — a length, an angle, an age — turns a shape grammar into something that can respond to conditions, and open L-systems can take input from a simulated environment.
They are used. Plant modelling for graphics, for agronomy, and for architecture runs on them, and the results are good.
What they do not do
They contain no mechanism whatsoever.
The turn angle is a parameter, not a consequence of anything. The branching ratio is chosen. Nothing competes for light, nothing is limited by water, nothing has to support its own weight, and no signal passes between parts. A tree that grew by these rules would be indifferent to being planted upside down.
That is not a criticism of L-systems, which were never claimed to be mechanistic. It is a criticism of what happens next: a picture this convincing invites the conclusion that the rules capture something about how the plant works, and it does not follow.
The general problem
Patterns are cheap, and visual conviction is a poor guide to explanatory power.
A great many rules produce branching trees. A great many produce spirals. A great many produce hexagonal tilings. Matching an output constrains the space of possible mechanisms far less than it feels like it does, and the more striking the match, the stronger the false impression.
This is the same caution that applies to the ferrofluid experiment, which produces spiral phyllotaxis with no biology in it, and to the dynamical model, which produces the golden angle without being told it. In every case the honest reading is the same: the model shows what kind of process could produce the pattern, and narrows the field of candidate mechanisms without selecting one.
What distinguishes a description from a mechanism
A useful test, since the distinction is doing so much work.
A description reproduces the output. Change the conditions and it has nothing to say unless one change the description too.
A mechanism predicts the response. Change the light, the water, the gravity, the hormone, and it says what should happen — and can be wrong.
By that test, Murray’s law is a mechanism: it derives a relation from stated costs and predicts what happens to a junction when the flow changes. An L-system is a description: it reproduces a shape and says nothing about what would happen to the plant in shade.
Both are worth having. The error is treating the second as though it were the first because its output is prettier.
Where the line actually falls
It is worth being fair to the modern versions, which are less naive than the classic ones.
Environmentally-sensitive L-systems let the rewriting depend on simulated light or space, so a branch that finds itself shaded develops differently. That is a genuine step toward mechanism, because the response to a condition is now in the model rather than in the parameters.
The line is not between L-systems and other formalisms; it is between models whose parameters encode the answer and models that derive it. A rewriting rule with a fixed branch angle encodes it. A model in which the angle emerges from competition for light derives it — and could be wrong about a plant, which is what makes it worth testing.