评估用于路边植被重建的种子混合物--丰富度和产地是侵蚀控制的预测因素

IF 1.3 Q4 ENGINEERING, ENVIRONMENTAL Journal of Ecological Engineering Pub Date : 2024-07-01 DOI:10.12911/22998993/187147
Anaël Mayeur, Florence Carpentier, Augustin Soki-Makilutila, Bruno Colas
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引用次数: 0

摘要

近几十年来,路边重新植被的做法发生了很大变化,其原因是需要更可靠、更具成本效益的操作,以及对将生物多样性纳入大型土地规划项目的关注不断升级。随着人们对提供生态系统服务和恢复能力的兴趣日益浓厚,人们开始考虑使用丰富的本地来源种子混合物进行重新植被,但这些混合物在满足实践者的侵蚀控制要求方面的功效仍未得到充分探索。本研究针对这一空白,在受控温室环境中调查了六种种子混合物的侵蚀控制潜力,这些种子混合物分为三种成分,具有不同的丰富度(标准<竞争<生物多样性),分别来自本地和非本地。为此,在 118 天的时间内评估了这些模式对植被覆盖率、植物密度和根部生物量生产的影响。在植被建立阶段,虽然原产地对植被覆盖度的影响尚无定论,但较丰富的生物多样性成分能更快地实现地面覆盖,这要归功于大叶草本植物的存在。与其他两种组合相比,更丰富的组合中植物的总体密度较低,但其本地版本的密度明显较高。研究结果表明,植被覆盖度和密度之间存在权衡关系,这是由物种内部和物种之间的竞争过程驱动的,特别是与获得光照有关的竞争过程。根部生物量的产生受种子来源的影响,当地混合种子的生物量更高,尤其是在成分更丰富的情况下。尽管生长条件对栽培品种有利,但野生和当地来源的种子仍显示出良好的效果,因此在路边植被重建工作中应更广泛地采用野生和当地来源的种子。面对不断变化的环境条件,这些研究结果为实践者优化重新植被战略和提高路边生态系统的恢复能力提供了启示。现在应该进行原地实地试验,以证实在路边管理和生态恢复的受控条件下取得的这些有希望的结果。
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Assessing Seed Mixtures for Roadside Revegetation – Richness and Origin as Predictors for Erosion Control
Roadside revegetation practices have evolved significantly in recent decades, spurred by the need for more reliable and cost-efficient operations and escalating concerns regarding biodiversity integration into large land-planning projects. While the use of rich and locally-sourced seed mixtures for revegetation is being considered with a rising interest to provide ecosystem services and resiliency, their efficacy in meeting practitioners’ erosion-control expec - tations remains underexplored. This study addressed this gap by investigating the erosion-control potential of six seed mixtures, categorized into three compositions with varying levels of richness (standard < competitive < bio-diversity) and sourced from local and non-local origins, in a controlled greenhouse environment. For this purpose, the effect of these modalities on vegetation cover, plant density and root biomass production was assessed over a period of 118 days. During the establishment phase, while the effect of the origin on cover was not conclusive, the richer Biodiversity composition achieved faster ground cover, attributed to the presence of large-leaved forbs. The overall density of plants was lower in the richer mix compared to the two others, but significantly higher in its local version. The findings underscored trade-offs between vegetation cover and density, driven by intra-and inter-specific competition processes, particularly related to access to light. Root biomass production was influenced by the origin of the seeds, with local mixes yielding higher biomass, particularly visible in the richer composition. In spite of growing conditions supposed to favor cultivars, wild and locally-sourced seeds showed promising results, advocating their broader adoption in roadside revegetation efforts. These findings provide insights for practitioners to optimize revegetation strategies and enhance roadside ecosystem resilience in the face of changing environmental conditions. In situ field trials should now be carried out to confirm these promising results obtained under controlled conditions for roadside management and ecological restoration.
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来源期刊
Journal of Ecological Engineering
Journal of Ecological Engineering ENGINEERING, ENVIRONMENTAL-
CiteScore
2.60
自引率
15.40%
发文量
379
审稿时长
8 weeks
期刊介绍: - Industrial and municipal waste management - Pro-ecological technologies and products - Energy-saving technologies - Environmental landscaping - Environmental monitoring - Climate change in the environment - Sustainable development - Processing and usage of mineral resources - Recovery of valuable materials and fuels - Surface water and groundwater management - Water and wastewater treatment - Smog and air pollution prevention - Protection and reclamation of soils - Reclamation and revitalization of degraded areas - Heavy metals in the environment - Renewable energy technologies - Environmental protection of rural areas - Restoration and protection of urban environment - Prevention of noise in the environment - Environmental life-cycle assessment (LCA) - Simulations and computer modeling for the environment
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