| ||Despite the common misconception of nearly static organisms, plants do interact continuously with the environment and with|
each other. It is fair to assume that during their evolution they developed particular features to overcome problems and to
exploit possibilities from environment. In this paper we introduce various quantitative measures based on recent advancements in complex network theory that allow to measure the effective similarities of various species. By using this approach on the similarity in fruit-typology ecological traits we can, for example, obtain a clear plant classification in a way similar to traditional taxonomic classification. This result is not trivial, since a similar analysis done on the basis of diaspore morphological properties do not provide any clear parameter to classify plants species. Complex network theory can then be used in order to determine which feature amongst many can be used to distinguish scope and possibly evolution of plants. Future uses of this approach range from functional classification to quantitative determination of plant communities in nature. This activity is made with the LINV Lab of Prof. S. Mancuso.
A different, but related activity concerns the use of networks as instrument to filter information in the case of medical patients. The data analysed come from Orthodontical Analysis. This activity is made in collaboration with Prof. Lorenzo Franchi , Dr. Marco Scazzocchio and MD Pietro Auconi
In this unit we use Complex Networks for natural plants in order to
- provide a different species classification
- show their channels of communication through volatile organic compounds (VOCs)
- compare longitudinal data with cross-sectional ones.