Effects of tree spacing and thinning on root reinforcement in mountain forests of the European Southern Alps

Cislaghi, Alessio; Alterio, Edoardo; Fogliata, Paolo; Rizzi, A.; Lingua, Emanuele; Vacchiano, Giorgio; Bischetti, Gian Battista; Sitzia, Tommaso

doi:10.1016/j.foreco.2020.118873

Cited by 25 publications

(17 citation statements)

References 64 publications

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“…Three forest types that are widely distributed in the Italian mountain areas (Cislaghi et al, 2021), uneven‐aged sweet chestnut ( Castanea sativa Mill. ), European beech ( Fagus sylvatica L.) and Norway spruce ( Picea abies [L.] H.…”

Section: Methodsmentioning

confidence: 99%

Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What's the best one?

et al. 2022

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In soil organic carbon (SOC) survey, the role of sampling approach is crucial and should not be underestimated. In this sense, the appropriateness of soil sampling by pedogenic horizons (PGH) and fixed depth layers (FDL) in forest is still a field of debate. The present work aimed to: (a) study the spatial variability of SOC concentrations and C stocks through PGH and FDL samplings in uneven-aged sweet chestnut, European beech and Norway spruce forests of the Apennine chain (Italy); (b) clarify the major advantages and drawbacks of sampling by PGH and FDL. In a representative area (18 Â 18 m) of each forest, a soil profile was investigated and eight additional sampling points were selected. The sampling was performed both by PGH and FDL (0-15 and 15-30 cm). For each forest, no SOC content and C stock differences in 0-30 cm soil depth were found between PGH (58.8 ± 5.3 g kg À1 and 103 ± 7 g ha À1 , respectively) and FDL (52.7 ± 4.3 g kg À1 and 117 ± 9 Mg ha À1 , respectively) sampling. However, under Norway spruce, PGH sampling pointed-out that C was mainly stored in Oe and Oa horizons (51.1 vs. 146 Mg ha À1 in the 0-30 cm layer). The higher coefficient of variation obtained when soil horizons were considered (from 19.2% to 72.8% for PGH and from 16.5% to 25.7% for FDL) suggested that PGH sampling preserved the information about the spatial variability within an ecosystem. The information loss about SOC vertical and spatial distribution would indicate the inability of FDL sampling to support decision-making plans addressed for sustainable use of soil resource.

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Section: Methodsmentioning

confidence: 99%

Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What's the best one?

et al. 2022

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“…Results of the present study (Fig. 6), Cislaghi et al (2021) and Moos et al (2016) indicated that, to maximise the protective effects of large trees, their spacing distance should be kept below 6 m (i.e., average stem density ≥ 256 stem ha -1 ).…”

Section: Suggestions For Landslide-prone Forest Management and Future...mentioning

confidence: 71%

“…Biotic interactions between trees influence tree community properties, including trees' species diversity (LaManna et al 2017), spatial distribution (Germany et al 2019), and stem density (Magee et al 2021). The likelihood of landslide occurrence has been related to tree species diversity (Genet et al 2010, Osman & Barakbah 2011, spatial distribution (Roering et al 2003, Cohen & Schwarz 2017, Cislaghi et al 2021, and stem density (Fan & Lai 2014, Moos et al 2016. It follows that tree-tree interactions should influence landslide occurrence through their effects on tree community properties.…”

Section: Introductionmentioning

confidence: 99%

The nurse-plant effect under the dislodgement stress of landslides

Yang¹,

Chang²,

Kc³

et al. 2023

iForest

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While the mitigating effects of trees on shallow landslide occurrence are well recognised, the impact of landslides on tree community structure and treetree interactions have received much less research attention. The structures of tree communities before and after landslides were compared in a 25-ha subtropical forest plot. Tree-tree interactions were examined by analysing the pre-and post-landslide spatial point patterns of large (DBH ≥ 20 cm) and small (1 cm ≤ DBH < 20 cm) tree cohorts. In landslide scarps, 35 (34%) of 104 large trees and 467 (13%) of 3,072 small trees survived. Large (L) and small (S) tree cohorts were paired together for spatial analyses, including pre-landslide (PL) (LPL-SPL), surviving (S) (LS-SS), and missing (M) large-small tree paired cohorts (LM-SM). We randomly selected trees from the pre-landslide tree cohorts to create two virtual paired cohorts, the L34%-S13% and L66%-S87% paired cohorts, whose population sizes were identical to the field-observed LS-SS and LM-SM paired cohorts respectively, but with random spatial patterns. Post-landslide survival rates of trees increased monotonically with DBH. Large trees dislodged by landslides scarcely reduced small-tree survival. Evidence for this included: (i) the distance from small trees to the nearest large trees of the LM-SM paired cohort did not differ significantly from that of the virtual L66%-S87% paired cohort; (ii) survival rates of small trees near LM individuals did not differ significantly from those without large trees nearby. Surviving large trees had positive effects on the survival of small trees, indicated by: (i) the distance from small trees to the nearest large trees of the LS-SS paired cohort was significantly lower than that of the virtual L34%-S13% paired cohort; (ii) SS individuals clumped around LS individuals, whereas the virtual L34%-S13% spatial relationship was random. Large trees prevent landslide dislodgement of adjacent small trees through the nurse-plant effect. Our study suggests that landslide damage in sloping forests may be reduced simply by constantly maintaining a critical density of large trees.

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“…However, its effectiveness requires better assessment through post-intervention environmental monitoring [1][2][3][4]. Here, root reinforcement is a mechanism through which forests contribute to the prevention and mitigation of shallow soil instabilities-one of the main hazards in mountain areas [5,6]. It is well recognized that roots reinforce soil and that the distribution of roots within vegetated slopes strongly influences the spatial distribution of soil strength [7].…”

Section: Introductionmentioning

confidence: 99%

Root Reinforcement Effect on Cover Slopes of Solid Waste Landfill in Soil Bioengineering

Park

Kim

Kang³

2021

Sustainability

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This study investigated the effect of vegetation plant roots on the stability of the cover slopes of solid waste landfills. A large direct shear test and a root tensile strength test were conducted to quantify the effect of rooted soil of revegetation plants on the increment in shear strength of the soil as a method to protect the cover slope of solid waste landfills. In the large direct shear test, an increase in the shear strength of the ground with the presence of roots was observed, and the root reinforcement proposed in the literature was modified and proposed by analyzing the correlation between the root diameter and the tensile strength according to water content. The stability of the slope revegetation of a landfill facility, considering the root reinforcement effect of revegetation, was calculated by conducting a slope stability analysis reflecting the unsaturated seepage analysis of rainfall conditions for various analysis conditions, such as the gradient, the degree of compactness, the thickness of the cover, and the rooted soil depth of the landfill facility.

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Effects of tree spacing and thinning on root reinforcement in mountain forests of the European Southern Alps

Cited by 25 publications

References 64 publications

Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What's the best one?

Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What's the best one?

The nurse-plant effect under the dislodgement stress of landslides

Root Reinforcement Effect on Cover Slopes of Solid Waste Landfill in Soil Bioengineering

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