Interference Competition and High Temperatures Reduce the Virulence of Fig Wasps and Stabilize a Fig-Wasp Mutualism

Fig trees are pollinated by fig wasps, which also oviposit in female flowers. The wasp larvae gall and eat developing seeds. Although fig trees benefit from allowing wasps to oviposit, because the wasp offspring disperse pollen, figs must prevent wasps fr

Fragmentation effects on diversity of wasp community and its impact on fig/fig wasp interaction in Ficus racemosa L.

Habitat fragmentation usually results in alteration of species composition or biological communities. However, little is known about the effect of habitat fragmentation on the fig/fig wasp system. In this study, we compared the structure of a fig wasp community and the interaction between figs and fig wasps of Ficus racemosa L. in a primary forest, a locally fragmented forest and a highly fragmented forest. Our results show that, in the highly fragmented forest, the proportion of pollinator wasps is lower and the proportion of non-pollinator wasps is higher compared with the primary forest and locally fragmented forest. The proportion of fruits without pollinator wasps in mature fruits is also greatly increased in the highly fragmented forest. The proportion of galls in all female flowers increases in the highly fragmented forest, whereas the proportion of viable seeds does not change considerably. The disruption of groups of fig trees results in a decrease in pollinator wasps and even might result in the extinction of pollinator wasps in some extreme cases, which may transform the reciprocal interaction between figs and fig wasps into a parasite/host system. Such an effect may lead to the local extinction of this keystone plant resource of rain forests in the process of evolution, and thereby, may change the structure and function of the tropical rain forest.

The Relationship between Decision Trees and the Scale of Train Data Set

Decision Trees need train samples in the train data set to get classification rules. If the number of train data was too small, the important information might be missed and thus the model could not explain the classification rules of data. While it is not affirmative that large scale of train data set can get well model. This Paper analysis the relationship between decision trees and the train data scale. We use nine decision tree algorithms to experiment the accuracy, complexity and robustness of decision tree algorithms. Some results are demonstrated.