| Summary: | The need to understand and predict Arctic environmental change has increased the demand to acquire comprehensive information for local communities, scientists, and policymakers. Broad reviews that summarize observations are an important tool to produce this pervasive knowledge on ecosystem properties and processes. However, our understanding about Arctic ecosystems is limited by a relatively sparse network of observations and research gaps that have not been fully identified. For example, the key drivers of fine-scale variability in the carbon cycle, which is an important ecosystem function in the Arctic, have not yet been synthesized. An improved understanding of the current knowledge in Arctic ecosystems is required to predict how Arctic ecosystems function in current and future conditions. In this thesis, I study the representativeness of field sampling locations, and knowledge gaps as well as drivers of fine-scale carbon cycling across the terrestrial Arctic. The first paper focuses on how field sampling locations are distributed across Arctic topographical, soil, and vegetation gradients within broad environmental science disciplines. In the second paper, I review the current state of knowledge in Arctic carbon dioxide (CO2) flux chamber studies which are used to measure fine-scale variability in gas exchange between the biosphere and the atmosphere. And in the third paper, I examine the drivers of fine-scale spatial variability in Arctic carbon cycling as a whole by studying both CO2 fluxes and carbon stocks, with a study design that includes in-situ climatic, soil, and plant community functional composition measurements from 80–220 plots across a tundra landscape. This thesis applies machine learning and Bayesian methods to understand the coverage of field sampling locations and drivers of carbon cycling, respectively. The underlying idea in this thesis is to examine research gaps across Arctic environmental gradients and chamber literature, explore the drivers of carbon cycling at a local scale, as well ...
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