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Contenu archivé le 2024-04-30

Predicting the benefits of landscape restoration - a planning perspective formetapopulation models

Objectif



Research objectives and content
Metapopulations are assemblages of local populations linked by occasional dispersal, and are widely recognised as important in the long-term persistence of many species. An important application of metapopulation theory is in the conservation of species in fragmenting landscapes. The metapopulation framework, applied at the landscape level, will enable us to investigate the best methods of preserving populations and species diversity in the current agricultural landscape, and to predict the benefits of investments in landscape restoration/rehabilitation. Butterflies are one of the best studied groups
regarding the effects of habitat fragmentation, and have been widely used in theoretical metapopulation models. A range of butterfly species will be investigated in this project, using mark-release- recapture studies, distribution/occupancy surveys and experimental manipulation of populations. The results will enable us to look at the ways in which butterflies move through the landscape. Metapopulation models will be linked to movement models and to GIS. The models developed during this project will be valuable in testing the impact of different landscape change scenarios.
Training content (objective, benefit and expected impact)
The main training component of the project will be in GIS, which is a valuable tool in any study of spatially structured populations. GIS-based models will allow us to identify characteristics of the landscape which increase or obstruct butterfly movements at the landscape scale.

Champ scientifique (EuroSciVoc)

Programme(s)

Thème(s)

Appel à propositions

Données non disponibles

Régime de financement

RGI - Research grants (individual fellowships)

Coordinateur

NORWEGIAN FOUNDATION FOR NATURE RESEARCH AND CULTURAL HERIT
Contribution de l’UE
Aucune donnée
Adresse
13,Dronningensgate 13
0105 OSLO
Norvège
Coût total
Aucune donnée

Participants (1)