Potentiality of extensive green roofs soils in sustaining Mediterranean annual dry grassland of the EU-Habitat 6220*
- Autori: Catalano, C.; Guarino, R.; Lo Verde, G.; Badalucco, L.; Palazzolo, E.; Laudicina, V.
- Anno di pubblicazione: 2015
- Tipologia: Proceedings
- OA Link: http://hdl.handle.net/10447/199060
Abstract
Nature-based solutions are defined as living solutions inspired by, continuously supported by and using nature (Final Report of the Horizon 2020 Expert Group on 'Nature-Based Solutions and Re-Naturing Cities'). In line with this statement and according to the European research and innovation policy agenda, green roofs (GRs) represent a fundamental requisite for urban sustainable development, being potential stepping stones for plants and animals, including those characterizing habitats of Community interest. In our work, we tested the suitability of green roofs in hosting Mediterranean annual dry grassland of the 92/43 ECC Habitat 6220* (pseudo-steppe with grasses and annuals of the Thero-Brachypodietea). The two investigated GRs respectively of 400 and 500 m2, are located 35 m above ground level and were built in the early 90s in Palermo on two different buildings. The growing medium used on the roofs was the typical Mediterranean red soil (Alfisol), common to the neighboring agricultural land. A total of 26 vegetation plots and 15 substrate samples were collected from the two GRs and, for comparison, in one neighboring orange grove and in four Natura 2000 sites of North-West Sicily (Mt. Pellegrino, Mt. Cofano, Cape Gallo and Cape St. Vito). In order to assess the potentiality of the GRs to host the target plant communities and the quality of the roof substrate, chemical and physical properties, as well as, some quality bioindicators (soil macro- and microarthropods, microbial biomass and activity) were determined for all the soil samples. Differences and similarities of the roof substrate with respect to its original milieu and that of the Habitat 6220* were then assessed by means of a multivariate statistical analysis and generalized linear regression models.