Lowland tundra plant stoichiometry is somewhat resilient decades following fire despite substantial and sustained shifts in community structure

Baillargeon, Natalie; Pold, Grace; Natali, Susan M.; Sistla, Seeta A.

doi:10.1080/15230430.2022.2121246

Cited by 3 publications

(3 citation statements)

References 44 publications

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“…Rather, plant biomass typically has been measured as part of individual research projects, each with its own focus and protocols. For instance, researchers have investigated how plant biomass is affected by climate 23 , 24 , wildfire 25 – 28 , herbivores 29 , 30 , and soil properties such as texture, nutrients, and pH 31 – 33 . Researchers have also measured plant biomass to assess ecosystem carbon storage 34 , 35 , evaluate terrestrial ecosystem models 36 , and map spatial patterns of plant biomass from landscape to biome scales by linking field and remote sensing measurements 37 – 40 .…”

Section: Background and Summarymentioning

confidence: 99%

The Arctic Plant Aboveground Biomass Synthesis Dataset

Berner,

Orndahl,

Rose

et al. 2024

Sci Data

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Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m−2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

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Section: Background and Summarymentioning

confidence: 99%

The Arctic Plant Aboveground Biomass Synthesis Dataset

Berner,

Orndahl,

Rose

et al. 2024

Sci Data

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“…), and graminoids (Carex spp. and Eriophorum angustifolium) (Baillargeon et al, 2022). The edge of degraded tundra included tundra bordering degraded permafrost, often wetter, mossier, and slightly subsided.…”

Section: Site Descriptionmentioning

confidence: 99%

Resolving heterogeneous fluxes from tundra halves the growing season carbon budget

Ludwig,

Schiferl,

Hung

et al. 2024

Biogeosciences

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Abstract. Landscapes are often assumed to be homogeneous when interpreting eddy covariance fluxes, which can lead to biases when gap-filling and scaling up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence net ecosystem exchange (NEE) of carbon dioxide (CO2) and methane (CH4) fluxes. With warming temperatures, Arctic ecosystems are changing from a net sink to a net source of carbon to the atmosphere in some locations, but the Arctic's carbon balance remains highly uncertain. In this study we report results from growing season NEE and CH4 fluxes from an eddy covariance tower in the Yukon–Kuskokwim Delta in Alaska. We used footprint models and Bayesian Markov chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent land-cover fluxes based on high-resolution land-cover maps of the region. We compared three types of footprint models and used two land-cover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. We used artificially created gaps of withheld observations to compare gap-filling performance using our derived land-cover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. We also compared resulting regional carbon budgets when scaling up observations using heterogeneous and homogeneous approaches. Traditional gap-filling methods performed worse at predicting artificially withheld gaps in NEE than those that accounted for heterogeneous landscapes, while there were only slight differences between footprint models and land-cover maps. We identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). We resolved distinct seasonality in tundra growing season NEE fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO2 sink by a factor of 2 and underestimated CH4 emissions by a factor of 2 when compared to scaling with any method that accounts for landscape heterogeneity. We show how Bayesian MCMC, analytical footprint models, and high-resolution land-cover maps can be leveraged to derive detailed land-cover carbon fluxes from eddy covariance time series. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic.

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“…), graminoids (Carex spp. and Eriophorum angustifolium) (Baillargeon et al, 2022); and edge of degraded tundra included tundra bordering degraded permafrost, often wetter, mossier, and slightly subsided. Degraded areas included isolated shallow depressions on peat plateaus, more evolved networks of flowpaths draining peat plateaus into wetlands, and recently drained waterbodies.…”

Section: Site Descriptionmentioning

confidence: 99%

Resolving heterogeneous fluxes from tundra halves the growing season carbon budget

Ludwig

Schiferl

Hung

et al. 2023

Preprint

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Abstract. Landscapes are often assumed to be homogeneous when interpreting eddy covariance fluxes, which can lead to biases when gap-filling and scaling-up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales, with variation in plant functional types, soil moisture, thaw depth, and microtopography, for example, influencing net ecosystem exchange (NEE) of carbon dioxide (CO2) and methane (CH4) fluxes. With warming temperatures, Arctic ecosystems could change from a net sink to a net source of carbon to the atmosphere in some locations, but the carbon balance remains highly uncertain. In this study we report results from growing season NEE and CH4 fluxes from an eddy covariance tower in the Yukon-Kuskokwim Delta in Alaska. We used footprint models and Bayesian Markov Chain Monte Carlo (MCMC) methods to un-mix tower observations into constituent landcover fluxes based on high resolution landcover maps of the tower region. We compared three types of footprint models and used two landcover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. We used artificially created gaps of withheld observations to compare gap-filling performance using our derived landcover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. We also compared resulting regional carbon budgets when scaling-up observations using heterogeneous and homogeneous approaches. Traditional gap-filling methods performed worse at predicting artificially withheld gaps in NEE than those that accounted for heterogeneous landscapes, while there were only slight differences between footprint models and landcover maps. We identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). We resolved distinct seasonality in tundra growing season NEE fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO2 sink by a factor of two and underestimated CH4 emissions by a factor of two when compared to scaling with any method that accounts for landscape heterogeneity. We show how Bayesian MCMC, analytical footprint models, and high resolution landcover maps can be leveraged to derive detailed landcover carbon fluxes from eddy covariance timeseries. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic.

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Lowland tundra plant stoichiometry is somewhat resilient decades following fire despite substantial and sustained shifts in community structure

Cited by 3 publications

References 44 publications

The Arctic Plant Aboveground Biomass Synthesis Dataset

The Arctic Plant Aboveground Biomass Synthesis Dataset

Resolving heterogeneous fluxes from tundra halves the growing season carbon budget

Resolving heterogeneous fluxes from tundra halves the growing season carbon budget

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