Gross and net nitrogen transformation rates and availability in late summer in tall shrub and birch hummock ecosystems of the Canadian low Arctic

Climate change is occurring most rapidly in the Arctic where warming has been twice as fast as the rest of the globe over the last few decades. Arctic soils contain a vast store of carbon and warmer arctic soils may mediate current atmospheric CO2 concentrations and global warming trends. Warmer soils could increase nutrient availability to plants, leading to increased primary production and sequestration of CO2. Presumably because of these effects of warming on shrub ecosystems, shrubs have been expanding across the arctic over the last 50 years, Arctic shrub expansion may track or cause changes in nutrient cycling and availability that favour growth of larger, denser shrubs. This study aimed at measuring gross and net nitrogen cycling rates, major soil nitrogen and carbon pool sizes, and elucidating controls on nutrient cycling and availability between a mesic birch (Betula nana) hummock tundra ecosystem and an ecosystem of dense, tall, birch (B. nana) shrubs. Nitrogen cycling and availability was enhanced at the tall shrub ecosystem compared to the birch hummock ecosystem. Net nitrogen immobilization by microbes was approximately threefold greater at the tall shrub ecosystem. This was in part because of larger microbial biomass nitrogen and carbon (interpreted as a larger microbial community) at the tall shrub ecosystem. Nitrogen inputs via litter were significantly larger at the tall shrub ecosystem and were hypothesized to be the major contributor to the higher dissolved organic and inorganic nitrogen pools in the soil at the tall shrub ecosystem. The results from this study suggest a positive feedback mechanism between litter nitrogen inputs and the enhancement of nitrogen cycling and availability as a driver of shrub expansion across the Arctic.

Remote Sensing of Vegetation Change Across a Latitudinal Gradient in the Canadian Arctic

Global air surface temperatures and precipitation have increased over the last several decades resulting in a trend of greening across the Circumpolar Arctic. The spatial variability of warming and the inherent effects on plant communities has not proven to be uniform or homogeneous on global or local scales. We can apply remote sensing vegetation indices such as the Normalized Difference Vegetation Index (NDVI) to map and monitor vegetation change (e.g., phenology, greening, percent cover, and biomass) over time. It is important to document how Arctic vegetation is changing, as it will have large implications related to global carbon and surface energy budgets. The research reported here examined vegetation greening across different spatial and temporal scales at two disparate Arctic sites: Apex River Watershed (ARW), Baffin Island, and Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU. To characterize the vegetation in the ARW, high spatial resolution WorldView-2 data were processed to create a supervised land-cover classification and model percent vegetation cover (PVC) (a similar process had been completed in a previous study for the CBAWO). Meanwhile, NDVI data spanning the past 30 years were derived from intermediate resolution Landsat data at the two Arctic sites. The land-cover classifications at both sites were used to examine the Landsat NDVI time series by vegetation class. Climate variables (i.e., temperature, precipitation and growing season length (GSL) were examined to explore the potential relationships of NDVI to climate warming. PVC was successfully modeled using high resolution data in the ARW. PVC and plant communities appear to reside along a moisture and altitudinal gradient. The NDVI time series demonstrated an overall significant increase in greening at the CBAWO (High Arctic site), specifically in the dry and mesic vegetation type. However, similar overall greening was not observed for the ARW (Low Arctic site). The overall increase in NDVI at the CBAWO was attributed to a significant increase in July temperatures, precipitation and GSL.