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

Abstract

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

Controlling factors of plant community composition with respect to the slope aspect gradient in the Qilian Mountains

“…Redundancy analysis (RDA), which is a form of direct gradient analysis (Ai et al 2017), was performed with a subset of environmental variables to estimate the fraction of the variation of the dependent variables (i.e., plant species composition) that can be attributed to each environmental variable by treating the explanatory environmental variables as regression covariables (Maccherini et al 2011). Redundancy analysis was performed using the CANOCO5.0 software (Microcomputer Power, Ithaca, New York, USA) to assess the relative influence of abiotic factors on the plant community composition (Yang et al 2018).…”

Gully head modelling: A Mediterranean badland case study

“…This conversion was actually made, independently from erosion, in 1400 because of the strong depopulation produced by the Black Death, from which Siena recovered completely only in 1700. Hence, we assume that the studied gully heads formed under the conditions depicted in the 1954 images with some of them becoming 'dormant' at a later time because of vegetation encroachment when grazing was abandoned (Marignani et al, 2008;Maccherini et al, 2011;and, more generally, Vila and Ibanez, 2011). This caused the sodium-leached topsoil horizon to disappear and the sodiumrich marine deposits to outcrop, with a further increase in soil erodibility and in erosion rate (Torri et al, 2002).…”

Erosion in Mediterranean landscapes: Changes and future challenges

“…The maintenance of these outdated landscapes is a major task for the immediate future, because their degradation will lead to an increase in sediment yield and to the degradation of a cultural legacy that could be productive for diverse purposes: to feed livestock; to produce, where appropriate, selected labour-intensive consumer crops; or to become sites of high tourism value. (Maccherini et al, 2011).…”