Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/49
Appears in Collections:eTheses from Faculty of Natural Sciences legacy departments
Title: Modelling forest landscape dynamics in Glen Affric, northern Scotland
Authors: Hope, Joseph C. E.
Supervisor(s): Osborne, Patrick E.
Tipping, Richard
Issue Date: Nov-2003
Publisher: University of Stirling
Abstract: Consideration of forest management at the landscape scale is essential if commitments to the conservation of biodiversity are to be upheld. The ecosystem management approach, developed largely in North America, has made use of various landscape modelling tools to assist in planning for biodiversity maintenance and ecological restoration. The roles of habitat suitability models, metapopulation models, spatially explicit population models (SEPMs) and forest landscape dynamics models (FLDMs) in the planning process are discussed and a review of forest dynamics models is presented. Potential is identified for developing landscape models in the UK for both landscape restoration projects and semi-natural woodland management. Glen Affric, in northern Scotland contains a large area of native pine and birch woodland and is the subject of a long-term restoration project. A new model, GALDR (Glen Affric Landscape Dynamics Reconstruction) is introduced and is believed to be the first FLDM developed for British woodland. The theory behind the model is described in detail and preliminary results and sensitivity analyses are presented. Furthermore, GALAM (Glen Affric Lichen Abundance Model), a new SEPM for the rare epiphytic lichen Bryoria furcellata is also described. Results of simulations from the linked GALDR and GALAM models are presented which shed light on the role of landscape heterogeneity in determining the dynamics of lichen habitats and populations. It is concluded that, whilst much work will be required to develop a management-oriented decision support system from the GALDR model, the modelling process may aid researchers in the identification of knowledge gaps in ecological theory relevant to management and restoration.
Type: Thesis or Dissertation
URI: http://hdl.handle.net/1893/49
Affiliation: School of Natural Sciences
Department of Environmental Science

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