Kelsey, Katharine C., Højlund Pedersen, Stine, Leffler, A. Joshua, Sexton, Joseph O., and Welker, Jeffrey M.. 2023. “ Snow and Vegetation Seasonality Influence Seasonal Trends of Leaf Nitrogen and Biomass in Arctic Tundra.” Ecosphere 14(5): e4515. https://doi.org/10.1002/ecs2.4515
Snow and vegetation seasonality influence seasonal trends of leaf nitrogen and biomass in Arctic tundra
|Author:||Kelsey, Katharine C.1; Pedersen, Stine Højlund2,3; Leffler, A. Joshua4;|
1Department of Geography and Environmental Science, University of Colorado Denver, Denver, Colorado, USA
2Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, USA
3Department of Biological Sciences, University of Alaska Anchorage, Anchorage, Alaska, USA
4Department of Natural Resource Management, South Dakota State University, Brookings, South Dakota, USA
5terraPulse, Inc, Gaithersburg, Maryland, USA
6Ecology and Genetics Research Unit, University of Oulu, Oulu, Finland
7University of the Arctic – UArctic, Rovaniemi, Finland
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023081596701
John Wiley & Sons,
|Publish Date:|| 2023-08-15
Climate change, including both increasing temperatures and changing snow regimes, is progressing rapidly in the Arctic, leading to changes in plant phenology and in the seasonal patterns of plant properties, such as tissue nitrogen (N) content and community aboveground biomass. However, significant knowledge gaps remain over how these seasonal patterns vary among Arctic plant functional groups (i.e., shrubs, grasses, and forbs) and across large geographical areas. We used three years of in situ field vegetation sampling from an 80,000-km² area in Arctic Alaska, remotely sensed vegetation data (daily normalized difference vegetation index [NDVI]), and modeled output of snow-free date to determine and model the seasonal trends and primary controls on leaf percent nitrogen and biomass (in grams per square meter) among Arctic vegetation functional groups. We determined relative vegetation phenology stage at a 500-m spatial scale resolution, defined as the number of days between the date of the seasonal maximum NDVI and the vegetation field sampling date, and relative snow phenology stage (90-m spatial scale) was determined as the number of days between the date of snow-free ground and the sampling date. Models including relative phenology stage were particularly important for explaining seasonal variability of %N in shrubs, graminoids, and forbs. Similarly, vegetation and snow phenology stages were also important for modeling seasonal biomass of shrubs and graminoids; however, for all functional groups, the models explained only a small amount of seasonal variability in biomass. Relative phenology stage was a stronger predictor of %N and biomass than geographic position, indicating that localized controls on phenology, acting at spatial scales of 500 m and smaller, are critical to understanding %N and biomass.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
1181 Ecology, evolutionary biology
This work was supported by the National Science Foundation Office of Polar Programs, Arctic System Science (Awards 1604249, 1604105, 1602440, 1602898, and 1604160).
© 2023 The Authors. Ecosphere published by Wiley Periodicals LLC on behalf of The Ecological Society of America. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.