Field observations and model simulations confirm the presence of unfrozen permafrost in shrub-dominated landscapes.

Worldwide trait database addresses long-standing questions about trait-environment relationships and provides an unprecedented digital resource to inform Earth system models.

Artificial intelligence applied to multiple remote sensing datasets accurately represents shrub distribution across hillslopes at the Kougarok field site.

Light-harvesting processes involved in photosynthesis by tundra plants are unexpectedly sensitive to growth at low temperatures.

Biochemical composition is proposed to improve process-based models of SOM degradation and climate feedbacks

A convolutional neural network (CNN) approach produced highly accurate vegetation classifications. Hyper-spectral datasets (e.g., AVIRIS) were most useful for our machine learning approaches. Accurate and high-resolution datasets generated using our approach are needed for Arctic models.

Several scaling strategies based on geomorphology were evaluated for complex polygonal landscapes.

Microtopographic variation among troughs, rims, and centers strongly affects the movement of surface water and snow and thereby affects soil water contents and active layer development.

Modeling approach improving representations of permafrost dynamics in evolving landscapes.

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