Towards accurate, data-driven models of microclimate for applications in ecology.

Current analyses of large-scale ecological patterns and their predictions under anthropogenic climate change are often based on free-air temperature and precipitation patterns with a coarse resolution. They thus fail to capture apparent temperatures and moisture conditions (cf. microclimate) experienced by living organisms within their habitats. We identified the need for a more holistic approach to microclimate data at the local scale for application in ecological studies. With the underlying project, we aim to establish a large-scale and multi-faceted microclimatic network in a subarctic mountain ecosystem, to formally study the role of topography, vegetation and land use on microclimatic conditions. As such, we want to improve our understanding of abiotic conditions in order to improve the accuracy of ecological models, and especially their ability to forecast changes in our vulnerable mountain ecosystems under global change. Using a network of state-of-the-art temperature and moisture loggers in the topographically complex landscape of the northern Scandinavian mountains, as well as drones equipped with thermal and multispectral cameras, we aim to obtain microclimatic data with a high spatiotemporal resolution and extent. This data will be converted into gridded microclimatic time series, from right where it matters for biodiversity, using a new unified statistical framework of spatiotemporal interpolations.

Keywords:MICROCLIMATE, MOUNTAINS

Disciplines:Climatology, Global ecology, Soil ecology