2010Restoration EcologyRequires access

Spontaneous Succession as a Restoration Tool for Maritime Cliff‐top Vegetation in Brittany, France

Jérôme Sawtschuk, Frédéric Bioret, Sébastien Gallet

Open publisher page 19 citations

Abstract

The impacts of human activities, especially those caused by tourism, have resulted in the destruction of coastal cliff‐top heathland and grassland vegetation. The loss of these natural habitats has led land managers to reduce human pressure and its impacts on the vegetation cover. Access has been restricted to stop the destruction, and restoration techniques are mainly based on the natural resilience of the vegetation. There have been few scientific reviews of the success of existing restoration operations through spontaneous succession. This article investigates existing restoration operations in five study areas where annual vegetation surveys have been recorded showing the long‐term trajectories of the spontaneous vegetation restoration. Statistical analyses based on multivariate analysis and Markov transition models allow us to describe the spontaneous successions, and to relate differences in the restoration trajectories to environmental factors.

About this research paper

What this paper is about

The impacts of human activities, especially those caused by tourism, have resulted in the destruction of coastal cliff‐top heathland and grassland vegetation. The loss of these natural habitats has led land managers to reduce human pressure and its impacts on the vegetation cover. Access has been restricted to stop the destruction, and restoration techniques are mainly based on the natural resilience of the vegetation. There have been few scientific reviews of the success of existing restoration operations through spontaneous succession. This article investigates existing restoration operations in five study areas where annual vegetation surveys have been recorded showing the long‐term trajectories of the spontaneous vegetation restoration. Statistical analyses based on multivariate analysis and Markov transition models allow us to describe the spontaneous successions, and to relate differences in the restoration trajectories to environmental factors.

Why it matters

OpenAlex reports 19 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

The impacts of human activities, especially those caused by tourism, have resulted in the destruction of coastal cliff‐top heathland and grassland vegetation. The loss of these natural habitats has led land managers to reduce human pressure and its impacts on the vegetation cover. Access has been restricted to stop the destruction, and restoration techniques are mainly based on the natural resilience of the vegetation. There have been few scientific reviews of the success of existing restoration operations through spontaneous succession. This article investigates existing restoration operations in five study areas where annual vegetation surveys have been recorded showing the long‐term trajectories of the spontaneous vegetation restoration. Statistical analyses based on multivariate analysis and Markov transition models allow us to describe the spontaneous successions, and to relate differences in the restoration trajectories to environmental factors.

Key concepts: Ecological succession, Vegetation (pathology), Cliff, Restoration ecology, Natural (archaeology), Grassland, Habitat, Environmental science

Related papers

Back to paper searchBrowse research topicsOriginal source
Spontaneous Succession as a Restoration Tool for Maritime Cliff‐top Vegetation in Brittany, France — Research Paper | ScholarLens