Her finner du våre tilgjengelige masteroppgaver fordelt på COAT sine ulike moduler. Ønsker du en annen oppgave ta kontakt med modullederen som passer ditt tema best.
COATs fjellrevmodul har en ledig masteroppgave for øyeblikket: What can we learn from tooth wear about diet and food limitation of red foxes in the low Arctic?
COATs smågnagermodul arbeider med klimaendringers påvirkning på tundraen, formidlet gjennom endringer i smågnagerpopulasjonenes sykluser.
Vi har flere masteroppgavetemaer tilgjengelig, for eksempel:
- How do rodents modify vegetation of their key habitats? Can this counteract climate change -driven vegetation changes? – working with an existing exclosure experiment with five years of data
- Do mild winters with icy snow lead to lower vole survival and dampened population cycles? This topic would focus on analyses of 15 years of vole and snow data.
- How are shrew population dynamics connected to small rodents? Some studies have found this, but their methods for measuring shrews have not specifically targeted these. We have a unique 10-year year-round camera trapping dataset that collects data on both rodents and shrews. Analyses of these data may include seasonal and multiannual dynamics of shrews and their connection to rodents.
- The same camera trapping dataset gives opportunities to study interactions between vole species, such as temporal synchrony and space use.
- Developing methods of measuring snow conditions using below-snow camera traps
- Spatial dynamics of a small rodent predator (the long-tailed skua). This thesis topic would focus on analyses of a 20-year dataset on skua nesting success.
COATs skog- og tregrensemodul har ledige oppgaver som omhandler effekter av klima og insektutbrudd på vekst hos trær, og nye metoder for kartlegging av skog og kratt basert på satellittdata og maskinlæring. For eksempel:
- Trees or shrubs? A field validation of machine learning based classifications of canopy types in Finnmark. With the advance of machine learning, vegetation classification based on remote sensing (RS) data sources is undergoing a rapid development. A pressing need in the monitoring and management of arctic and tree line ecosystems is a better ability to monitor changes in canopy cover of both shrubs and trees in a manner which is both cost efficient and flexible in terms of using different types of RS data as available. COAT is collaborating with RS experts and local management stakeholders on developing new methodologies for canopy mapping in Northern Norway and is providing the ecological data necessary to validate RS classifications. We are looking for a dedicated student with interest in RS and vegetation to participate in field sampling of new validation data during August 2025 and to evaluate the performance of RS products against field conditions.
- Chronologies of canopy changes in Finnmark. Finnmark has a rich legacy of aerial photos dating back to the 1960’s. Within this period, canopy state changes have occurred both in the form of advances (increase in trees and shrubs) and retreats (decrease in trees and shrubs). The primary causes of canopy change in Finnmark are regional scale insect outbreaks causing forest and shrub mortality, browsing from large herbivores such as reindeer and moose, climate driven encroachment, and human land use. We are looking for a student interested in landscape scale processes and/or remote sensing, to build chronologies of canopy changes, primarily based on aerial photos, to investigate drivers of landscape scale canopy state changes. COATs maintain several large scale well stratified field survey designs which can provide additional ground data. This is an opportunity both for a highly technically oriented student keen to work on automated classification workflows and/or machine learning, or for someone keen om a more manual approach targeted at COATs regional monitoring localities.