One hitherto intractable problem in studying mast seeding (synchronous intermittent heavy flowering by a population of perennial plants) is determining the relative roles of weather, plant reserves, and evolutionary selective pressures such as predator satiation. We parameterize a mechanistic resource-based model for mast seeding in Chionochloa pallens (Poaceae) using a long-term individually structured data set. Each plant's energy reserves were reconstructed using annual inputs (growing degree days), outputs (flowering), and a novel regression technique. This allowed the estimation of the parameters that control internal plant resource dynamics, and thereby allowed different models for masting to be tested against each other. Models based only on plant size, season degree days, and/or climatic cues (warm January temperatures) fail to reproduce the pattern of autocovariation in individual flowering and the high levels of flowering synchrony seen in the field. This shows that resource-matching or simple cue-based models cannot account for this example of mast seeding. In contrast, the resource-based model pulsed by a simple climate cue accurately describes both individual-level and population-level aspects of the data. The fitted resource-based model, in the absence of environmental forcing, has chaotic (but often statistically periodic) dynamics. Environmental forcing synchronizes individual reproduction, and the models predict highly variable seed production in close agreement with the data. An evolutionary model shows that the chaotic internal resource dynamics, as predicted by the fitted model, is selectively advantageous provided that adult mortality is low and seeds survive for more than 1 yr, both of which are true for C. pallens. Highly variable masting and chaotic dynamics appear to be advantageous in this case because they reduce seed losses to specialist seed predators, while balancing the costs of missed reproductive events
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