Predicting emergence, chorusing, and oviposition of periodical cicadas.

Periodical cicadas in the genus Magicicada have an unusual life history that includes an exceptionally long life cycle and a massive, synchronized emergence. Distributions in woodland habitat are extremely patchy, and an unresolved problem is how the patchiness is generated and maintained. We undertook a study in Princeton, New Jersey, USA, to determine if habitat factors such as tree height, distance to the nearest neighbor, and amount of foliage influence distributions of nymphal emergences, centers of chorusing aggregations, and oviposition sites. Emerging nymphs were counted, chorus centers were identified by measuring decibel levels of their songs, and oviposition was estimated from the foliage "flags" that are produced by trees in response to the damage caused by egg-laying. All three distributions were mapped on 50 trees in a human-managed area of 75 x 130 m. We were unable to identify habitat features that were associated with the distribution of emergences, except that tulip trees (Liriodendron) had significantly fewer emergences than two other genera, ash (Fraxinus) and beech (Fagus). Using multiple linear regression analysis, we found that the distribution of emergences was the best predictor of the distribution of chorus centers and oviposition sites. In an analysis of ash trees alone, tree height and spacing, in addition to cicada emergences, predicted chorus centers and oviposition incidence. Thus the distribution of the offspring generation (oviposition) is shown to be similar to the parental generation (emergence). We suggest that their stability is maintained across generations by extremely low dispersal and mutual attraction. Aggregations may be thought of as self-perpetuating, randomly distributed assemblages, initially established through the interaction of factors, including successful exploitation of underground resources, and avoidance of fungal disease and aboveground predation in the development of high-density mating aggregations.