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Data from: Trait-based selection of nurse plants to restore ecosystem functions in mine tailings

Cite this dataset

Navarro Cano, Jose A.; Verdú, Miguel; Goberna, Marta (2018). Data from: Trait-based selection of nurse plants to restore ecosystem functions in mine tailings [Dataset]. Dryad. https://doi.org/10.5061/dryad.j70qf

Abstract

1.Metal mining in drylands generates waste tailings with high toxicity, physical instability, as well as water and thermal stresses, that hamper their biological colonisation. This limits the restoration of ecosystem functions that are essential to re-integrate these artificial micro-deserts within the landscape matrix. 2.We assessed the functional role of local nurse plant species and their traits to restore ecosystem functions related to soil fertility, soil microbial productivity and the reduction of abiotic stress. We sampled 30 metalliferous tailings in a mining district from semiarid Spain to detect nurse plant species and quantify their ability to promote essential functions from their establishment on the barren substrate up to the adult stage. 3.We found 11 plant species acting as nurses out of 102 species able to colonise barren soils. Ten nurses further triggered a cascade of effects increasing soil fertility and microbial productivity and/or lowering soil abiotic stress. 4.Plant species with larger life forms and longer periods of establishment since tailing abandonment contributed the most to the promotion of ecosystem functions. C4 plant species developing root systems with lower intensivity and depth: laterality ratios, as well as leaves with lower carbon: nitrogen ratios (C:N) induced a faster recovery of ecosystem functions. 5.Synthesis and applications. We propose a protocol for selecting key species to be used in restoration programs based on their ability to restore ecosystem functions under extremely stressful conditions. We encourage combination of multiple target species with complementary traits in order to reinforce the rehabilitation of ecosystem functions.

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