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Linking landscape genetic structure with local adaptation to changing environments

Ziel

Climate change and habitat fragmentation are considered key pressures on biodiversity. Two spatial population processes are important determinants of a species’ vulnerability to habitat fragmentation and climate change: 1) the species’ ability to disperse and colonise new areas, and 2) the species’ ability to adapt to these new areas. This study will evaluate the sensitivity of the damselfly, Ishnura elegans, to habitat fragmentation versus land-use and climate change effects and assess the species’ expansion and adaptive potential using cutting edge genomics technologies. This project will utilise next-generation DNA sequencing data which allows for whole-genome analysis of evolutionary processes within naturally occurring populations under variable, and changing environments. Our sampling design will cover the species’ northern range limit located in south and central Sweden, which provides a dynamic environmental gradient that is at the forefront of range expansion under projected climate change. The overarching aim of the proposal is to identify topographic, land-use and climatic factors that determine dispersal patterns that are involved in local adaptation to environmental conditions in I. elegans. In doing so, this project will use real and simulated genetic and landscape data to test current theoretical and methodological advances concerning how organisms will adapt to widespread environmental changes via contemporary evolution, with practical implications for predicting climate change impacts on organisms.

Aufforderung zur Vorschlagseinreichung

FP7-PEOPLE-2013-IIF
Andere Projekte für diesen Aufruf anzeigen

Koordinator

MAX IV Laboratory, Lund University
EU-Beitrag
€ 197 811,60
Adresse
Paradisgatan 5c
22100 LUND
Schweden

Auf der Karte ansehen

Region
Södra Sverige Sydsverige Skåne län
Aktivitätstyp
Higher or Secondary Education Establishments
Kontakt Verwaltung
Peter Stenild Matthesen (Mr.)
Links
Gesamtkosten
Keine Daten