Persistence of arctic-alpine flora during 24,000 years of environmental change in the Polar Urals

Plants adapted to extreme conditions can be at high risk from climate change; arctic-alpine plants, in particular, could "run out of space" as they are out-competed by expansion of woody vegetation. Mountain regions could potentially provide safe sites for arctic-alpine plants in a warmer climate, but empirical evidence is fragmentary. Here we present a 24,000-year record of species persistence based on sedimentary ancient DNA (sedaDNA) from Lake Bolshoye Shchuchye (Polar Urals). We provide robust evidence of long-term persistence of arctic-alpine plants through large-magnitude climate changes but document a decline in their diversity during a past expansion of woody vegetation. Nevertheless, most of the plants that were present during the last glacial interval, including all of the arctic-alpines, are still found in the region today. This underlines the conservation significance of mountain landscapes via their provision of a range of habitats that confer resilience to climate change, particularly for arctic-alpine taxa.

Combining LiDAR and IKONOS data for eco-hydrological classification of an ombrotrophic peatland

Remote sensing techniques have potential for peatland monitoring, but most previous work has focused on spectral approaches that often result in poor discrimination of cover types and neglect structural information. Peatlands contain structural "microtopes" (e.g., hummocks and hollows) which are linked to hydrology, biodiversity and carbon sequestration, and information on surface structure is thus a useful proxy for peatland condition. The objective of this work was to develop and test a new eco-hydrological mapping technique for ombrotrophic (rain-fed) peatlands using a combined spectral-structural remote sensing approach. The study site was Wedholme Flow, Cumbria, UK. Airborne light dectection and ranging (LiDAR) data were used with IKONOS data in a combined multispectral-structural approach for mapping peatland condition classes. LiDAR data were preprocessed so that spatial estimates of minimum and maximum land surface height, variance and semi-variance (from semi-variogram analysis) were extracted. These were assimilated alongside IKONOS data into a maximum likelihood classification procedure, and thematic outputs were compared. Ecological survey data were used to validate the results. Considerable improvements in thematic separation of peatland classes were achieved when spatially-distributed measurements of LiDAR variance or semi-variance were included. Specifically, the classification accuracy improved from 71.8% (IKONOS data only) to 88.0% when a LiDAR semi-variance product was used. Of note was the improved delineation of management classes (including Eriophorum bog, active raised bog and degraded raised bog). The application of a combined textural-optical approach can improve land cover mapping in areas where reliance on purely spectral discrimination approaches would otherwise result in considerable thematic uncertainty.