Integrating snow science and wildlife ecology in Arctic-boreal North America

Snow covers Arctic and boreal regions (ABRs) for approximately 9 months of the year, thus snowscapes dominate the form and function of tundra and boreal ecosystems. In recent decades, Arctic warming has changed the snowcover's spatial extent and distribution, as well as its seasonal timing and...

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Bibliographic Details
Published in:Environmental research letters 2019-01, Vol.14 (1), p.10401
Main Authors: Boelman, Natalie T, Liston, Glen E, Gurarie, Eliezer, Meddens, Arjan J H, Mahoney, Peter J, Kirchner, Peter B, Bohrer, Gil, Brinkman, Todd J, Cosgrove, Chris L, Eitel, Jan U H, Hebblewhite, Mark, Kimball, John S, LaPoint, Scott, Nolin, Anne W, Pedersen, Stine Højlund, Prugh, Laura R, Reinking, Adele K, Vierling, Lee A
Format: Article
Language:English
Subjects:
ABoVE
Airborne sensing
Algorithms
Arctic boreal vulnerability experiment
caribou
Dall sheep
In situ measurement
Physical characteristics
Physical properties
polar bear
Remote sensing
Snow
Snow cover
Snowpack
Tundra
Wildlife
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Summary:Snow covers Arctic and boreal regions (ABRs) for approximately 9 months of the year, thus snowscapes dominate the form and function of tundra and boreal ecosystems. In recent decades, Arctic warming has changed the snowcover's spatial extent and distribution, as well as its seasonal timing and duration, while also altering the physical characteristics of the snowpack. Understanding the little studied effects of changing snowscapes on its wildlife communities is critical. The goal of this paper is to demonstrate the urgent need for, and suggest an approach for developing, an improved suite of temporally evolving, spatially distributed snow products to help understand how dynamics in snowscape properties impact wildlife, with a specific focus on Alaska and northwestern Canada. Via consideration of existing knowledge of wildlife-snow interactions, currently available snow products for focus region, and results of three case studies, we conclude that improving snow science in the ABR will be best achieved by focusing efforts on developing data-model fusion approaches to produce fit-for-purpose snow products that include, but are not limited to, wildlife ecology. The relative wealth of coordinated in situ measurements, airborne and satellite remote sensing data, and modeling tools being collected and developed as part of NASA's Arctic Boreal Vulnerability Experiment and SnowEx campaigns, for example, provide a data rich environment for developing and testing new remote sensing algorithms and retrievals of snowscape properties.
ISSN:1748-9326
1748-9326
DOI:10.1088/1748-9326/aaeec1