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Abstract Photosynthesis of terrestrial ecosystems in the Arctic-Boreal region is a critical part of the global carbon cycle. Solar-induced chlorophyll Fluorescence (SIF), a promising proxy for photosynthesis with physiological insight, has been used to track gross primary production (GPP) at regional scales. Recent studies have constructed empirical relationships between SIF and eddy covariance-derived GPP as a first step to predicting global GPP. However, high latitudes pose two specific challenges: (a) Unique plant species and land cover types in the Arctic–Boreal region are not included in the generalized SIF-GPP relationship from lower latitudes, and (b) the complex terrain and sub-pixel land cover further complicate the interpretation of the SIF-GPP relationship. In this study, we focused on the Arctic-Boreal vulnerability experiment (ABoVE) domain and evaluated the empirical relationships between SIF for high latitudes from the TROPOspheric Monitoring Instrument (TROPOMI) and a state-of-the-art machine learning GPP product (FluxCom). For the first time, we report the regression slope, linear correlation coefficient, and the goodness of the fit of SIF-GPP relationships for Arctic-Boreal land cover types with extensive spatial coverage. We found several potential issues specific to the Arctic-Boreal region that should be considered: (a) unrealistically high FluxCom GPP due to the presence of snow and water at the subpixel scale; (b) changing biomass distribution and SIF-GPP relationship along elevational gradients, and (c) limited perspective and misrepresentation of heterogeneous land cover across spatial resolutions. Taken together, our results will help improve the estimation of GPP using SIF in terrestrial biosphere models and cope with model-data uncertainties in the Arctic-Boreal region. 

Soil temperatures play an important role in determining the distribution and function of organisms. However, soil temperature is decoupled from air temperature and varies widely in space. Characterizing and predicting soil temperature requires large and expensive networks of data loggers. We developed an open-source soil temperature data logger and created online resources to ensure our design was accessible. We tested data loggers constructed by students, with little prior electronics experience, in the lab, and in the field in Alaska. The do-it-yourself (DIY) data logger was comparably accurate to a commercial system with a mean absolute error of 2% from −20–0 °C and 1% from 0–20 °C. They captured accurate soil temperature data and performed reliably in the field with less than 10% failing in the first year of deployment. The DIY loggers were ~1.7–7 times less expensive than commercial systems. This work has the potential to increase the spatial resolution of soil temperature monitoring and serve as a powerful educational tool. The DIY soil temperature data logger will reduce data collection costs and improve our understanding of species distributions and ecological processes. It also provides an educational resource to enhance STEM, accessibility, inclusivity, and engagement. 

Some rhizomatous grass and sedge species form tussocks that impact ecosystem structure and function. Despite their importance, tussock development and size controls are poorly understood due to the decadal to centennial timescales over which tussocks form.

We explored mechanisms regulating tussock development and size in a ubiquitous arctic tussock sedge (Eriophorum vaginatum) using field observations and a mass balance model coupled with a tiller population model. Model–data fusion was used to quantify parameter and prediction uncertainty, determine model sensitivity, and test hypotheses on the factors regulating tussock size.

The model accurately captured the dynamics of tussock development, characteristics, and size observed in the field. Tussock growth approached maximal size within several decades, which was determined by feedbacks between the mass balance of tussock root necromass and density‐dependent tillering. The model also predicted that maximal tussock size was primarily regulated by tiller root productivity and necromass bulk density and less so by tiller demography. These predictions were corroborated by field observations of tussock biomass and root characteristics.

The study highlights the importance of belowground processes in regulating tussock development and size and enhances our understanding of the influence of tussocks on arctic ecosystem structure and function.

 

Across forests, photosynthesis and woody growth respond to different climate cues. 

Foundation species have disproportionately large impacts on ecosystem structure and function. As a result, future changes to their distribution may be important determinants of ecosystem carbon (C) cycling in a warmer world. We assessed the role of a foundation tussock sedge (Eriophorum vaginatum) as a climatically vulnerable C stock using field data, a machine learning ecological niche model, and an ensemble of terrestrial biosphere models (TBMs). Field data indicated that tussock density has decreased by ∼0.97 tussocks per m²over the past ∼38 years on Alaska’s North Slope from ∼1981 to 2019. This declining trend is concerning because tussocks are a large Arctic C stock, which enhances soil organic layer C stocks by 6.9% on average and represents 745 Tg C across our study area. By 2100, we project that changes in tussock density may decrease the tussock C stock by 41% in regions where tussocks are currently abundant (e.g. −0.8 tussocks per m²and −85 Tg C on the North Slope) and may increase the tussock C stock by 46% in regions where tussocks are currently scarce (e.g. +0.9 tussocks per m²and +81 Tg C on Victoria Island). These climate-induced changes to the tussock C stock were comparable to, but sometimes opposite in sign, to vegetation C stock changes predicted by an ensemble of TBMs. Our results illustrate the important role of tussocks as a foundation species in determining future Arctic C stocks and highlight the need for better representation of this species in TBMs.