Grazing reduces the capacity of Landscape Function Analysis to predict regional-scale nutrient availability or decomposition, but not total nutrient pools

10 páginas.- 5 figuras.- 1 tabla.- referencias.- Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecolind.2018.03.034

Open Access in https://zenodo.org/record/1323892

The Nutrient Cycling Index (hereafter ‘Nutrient Index’) derived from Landscape Function Analysis (LFA) is used extensively by land managers worldwide to obtain rapid and cost-effective information on soil condition and nutrient status in terrestrial ecosystems. Despite its utility, relatively little is known about its reliability under different management conditions (e.g. grazing) or across different climatic zones (aridity). Here we correlated the Nutrient Index, comprising measures of biocrust cover, plant basal cover, soil roughness and three attributes of surface litter cover, with empirical data on measures of soil total nutrient pools (C and N), nutrient availability (labile C, inorganic N and P), and decomposition-related enzymes at 151 locations from eastern Australia varying in grazing intensity and climatic conditions. Grazing intensity was assessed by measuring current grazing (dung production by the herbivores cattle, sheep/goats, kangaroos and rabbits), and historic grazing (the total area of livestock tracks leading from water). We used aridity (the relationship between precipitation and potential evapotranspiration) as a measure of climate. On average, the Nutrient Index was positively associated with total nutrient pools, nutrient availability and decomposition enzymes. However, further statistical modelling indicated that grazing intensity strongly reduced the link between the index and decomposition enzymes, labile C and inorganic P, but not with total nutrient pools. This grazing effect was predominantly due to cattle. Conversely, aridity had no significant effect on the predictive power of the index, suggesting that it could be used across different aridity conditions in natural ecosystems as a reliable predictor of soil health. Overall, our study reveals that the Nutrient Index is a robust predictor of total nutrient pools across different aridity and grazing conditions, but not for predicting nutrient availability or decomposition in environments heavily grazed by livestock.

M. D-B. was supported by the Marie Sklodowska-Curie Actions of the Horizon 2020 Framework Programme H2020-MSCA-IF-2016 under REA Grant Agreement N° 702057.

Peer reviewed