Determinants of plant community composition of remnant biancane badlands: a hierarchical approach to quantify species-environment relationships


  • Co-ordinating Editor: Meelis Partel

  • Maccherini, S. (corresponding author, BIOCONNET, Biodiversity and Conservation Network, Department of Environmental Science ‘G.Sarfatti’, University of Siena, Via P.A. Mattioli 4, I–53100 Siena, Italy
    Santi, E. ( IRPI-CNR, Via Madonna Alta 126, I–06128 Perugia, Italy
    Marignani, M. ( Department of Environmental Biology, ‘Sapienza’ University of Rome, Piazzale Aldo Moro 5, I–00185 Rome, Italy
    Gioria, M. ( School of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland
    Renzi, M. (renzi2@unisi.i): Research Centre for lagoon ecology, fisheries and acquaculture, University of Siena, Polo Universitario Grossetano, via Lungolago dei Pescatori, I–58015 Orbetello, Italy
    Rocchini, D. (, Fondazione Edmund Mach, Research and Innovation Centre, Department of Biodiversity and Molecular Ecology, GIS and Remote Sensing Unit, Via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy
    Torri, D. ( IRPI-CNR, Via Madonna Alta 126, I–06128 Perugia, Italy
    Tundo, J. ( & Honnay, O. ( Laboratory of Plant Ecology, Biology Department, University of Leuven, Arenbergpark 31, B–3001 Heverlee, Belgium.


Question: Which environmental variables best explain patterns in the vegetation of biancane badlands? What is the role of spatial scales in structuring the vegetation of biancane badlands within the agricultural matrix?

Location: Five biancane badlands in Central Italy (Tuscany).

Methods: An object-oriented approach on high-resolution multispectral images was used to classify physiognomic vegetation types in five biancane badlands. Within each badland, data on vascular plant species abundance were collected using a stratified random design. Variation partitioning based on partial redundancy analysis was used to evaluate the contribution of three sets of environmental predictors, recorded at the spatial scales of plot, patch and biancane badland in explaining patterns in plant community composition.

Results: Environmental variables included in the final model – electrical conductivity and carbon/nitrogen ratio (plot scale), shape index (patch scale) and area (biancane badland scale) – accounted for 15.5% of the total variation in plant community composition. Soil characteristics measured at the plot level explained the majority of variation. In the smallest badlands, Bromus erectus perennial grasslands were absent, while annual grasslands, linked with harsh soil conditions (i.e. high soil salinity), were not affected by either the surface area of biancane badlands or by the soil nitrogen availability.

Conclusions: The identification of the major predictors of patterns in remnant vegetation requires conducting investigations at multiple spatial scale. Management strategies should operate at different spatial scale, preventing any further reduction in the size of existing badlands and relying on habitat- instead of area-focused conservation practices.