The basis for this research area is that patterns in fluctuations in population size of single species will characterize the form of the distribution of abundances of the species constituting the whole community. A change in the environment will therefore affect the species abundance distribution. In this research area we therefore focus on processes affecting the shape of species abundance distribution.
An important topic is also how stochastic fluctuations in the environment will affect the vertical and horizontal trophic interactions among species. These research questions require that the single-species approaches in the other two research areas (population ecology, evolutionary biology) are extended to include multi-species interactions. An important focus is therefore to implement demographic and evolutionary models in the analyses of interspecific interactions.
REINCLIM 2012-2015 - Predicting effects of climate change on Svalbard reindeer population dynamics: a mechanistic approach
Aim: To examine how community dynamics in time and space of different taxa along ecological gradient are affected by environmental stochasticity.
- Associate Professor Vidar Grøtan
- Professor Otso Ovaskainen
- Professor Bob O'Hara
- Senior researcher Ola Diserud
- Associate Professor Anders Gravbrøt Finstad
- Postdoc Christophe Coste
- PhD candidate Caitlin Mandeville
- PhD candidate Sam Perrin
- PhD candidate Lisa Sandal
- PhD candidate Tanja Pedersen
- PhD candidate Bert van der Veen
Dynamics of Arctic ecosystems
We do research in the intersection between ecology, behaviour, and evolution. The current main aim is to combine demographic modelling of keystone species and multi-species stochastic approaches to disentangle how community dynamics are influenced by trophic interactions and common versus species-specific environmental noise linked to climate change.
Dynamics of interacting species
Aim: To understand stochastic population dynamics in the presence of species interactions, using a combination of theoretical modeling and data analysis